Predictive overlay network architecture

ABSTRACT

The predictive overlay network architecture of the present invention improves the performance of applications distributing digital content among nodes of an underlying network such as the Internet by establishing and reconfiguring overlay network topologies over which associated content items are distributed. The present invention addresses not only frequently changing network congestion, but also interdependencies among nodes and links of prospective overlay network topologies. The present invention provides a prediction engine that monitors metrics and predicts the relay capacity of individual nodes and links (as well as demand of destination nodes) over time to reflect the extent to which the relaying of content among the nodes of an overlay network will be impacted by (current or future) underlying network congestion. The present invention further provides a topology selector that addresses node and link interdependencies while redistributing excess capacity to determine an overlay network topology that satisfies application-specific performance criteria.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional patent applicationSer. No. 62/488,502 filed Apr. 21, 2017, and U.S. provisional patentapplication Ser. No. 62/655,703 filed Apr. 10, 2018, the disclosures ofwhich are hereby incorporated by reference as if fully set forth herein.

I. BACKGROUND Field of Art

The present invention relates generally to the distribution of digitalcontent among nodes of an overlay network built on top of an underlyingnetwork such as the Internet, and more particularly to a predictiveoverlay network architecture that determines overlay network topologiesthat satisfy defined application-specific performance criteria byaddressing frequently changing underlying network congestion in thecontext of performance interdependencies among the nodes and links of anoverlay network.

Description of Related Art

A. The Problem of Network Congestion

At its most basic level, a computer network consists of multiple networkdevices (nodes) that are interconnected, directly or indirectly, for thepurpose of exchanging data or information (used interchangeably herein)and sharing resources provided by the network nodes. For example, twocomputers and a network printer connected to a network switch form asimple “local area network” (LAN) that enables users of both computersto share the printing resources provided by the network printer.

In this simple network, although both computers and the network printerare connected directly to the network switch, but connected onlyindirectly to one another, all are considered to be nodes on the LAN.The same is true whether the connections are made via wired or wirelessmedia.

Even in this simple LAN, network congestion occurs when one or bothcomputers send sufficiently large amounts of information to the networkprinter during a given period of time, resulting in printing delays andpotential failure of individual printing jobs (e.g., if the networkprinter's memory buffer becomes overloaded and network traffic exceedsthe printer's designed capacity). To address the problem of networkcongestion, particularly as more computers are added to the LAN, onemight introduce an additional network printer and software to “loadbalance” the print requests from the computers among the shared networkprinters—i.e., to increase supply and distribute the demand.

As will become apparent, the problem of network congestion at networknodes that provide shared resources becomes exponentially more complexas the underlying computer network increases in size and scope. Forexample, in addition to supporting network printing, devices on a LANmay provide additional network functionality such as file transfer,email, videoconferencing and other network applications and services.Shared use of this additional functionality by network nodes inevitablyexacerbates the problem of network congestion—as the “demand” of nodesconsuming this shared functionality routinely exceeds the “supply” orcapacity of individual nodes to provide and distribute suchfunctionality.

When an underlying network is expanded beyond a LAN to include morenodes at different physical locations (operated, for example, by variousindividual, commercial, governmental and other entities), networkrouters are deployed to enable the interconnection of multiple computernetworks to form a “wide area network” (WAN). The Internet—the mostpopular and heavily utilized WAN (i.e., a network ofnetworks)—interconnects billions of devices around the world andprovides the underlying infrastructure that supports a vast array ofshared network applications and services (referred to herein simply as“applications”).

Due its historical evolution as an ad hoc network with little or nocentralized control, the Internet is rife with network congestion issuesthat are difficult to address holistically. In particular, the routingof information among network nodes is decentralized. Routing decisionsare made in a distributed fashion by “intermediate routing nodes”(routers, switches, bridges, gateways, firewalls, etc., provided by manydifferent entities) that implement various distributed routingalgorithms.

As a result, while each router or other intermediate routing nodedetermines the “next hop” node to which it will transmit information, nocentralized entity determines the entire path (i.e., the set ofindividual “hops” between two nodes) that information traverses from a“source” node to a “destination” node. Moreover, at present, the entiretopology of network nodes on the Internet, including theirinterconnections, cannot feasibly be determined by any such entity.

To distribute information (also referred to herein as “digital content”)on the Internet, the information is divided into smaller packets thatare individually routed in accordance with an “Internet Protocol” (IP)addressing scheme that identifies each network node by a unique IPaddress. When one network node (node A) sends information to anothernetwork node (node B), that information typically is divided intomultiple IP packets, each addressed with the destination IP address ofthe destination node (node B), but each potentially traversing adifferent path (hops among various intermediate routing nodes) from nodeA to node B, where these packets are reassembled.

Because these intermediate routing nodes are shared resources utilizedat any given time by many other network nodes participating in a widerange of applications (including, for example, web browsing, filetransfer, email, telephony, video streaming, etc.), network congestionat one or more of these shared intermediate routing nodes is quitecommon. As a result, the ability of a source node to transferinformation to a destination node is negatively impacted by this networkcongestion, as the information encounters delays as it is distributedthrough these shared intermediate routing nodes. Such network congestionmay occur as the result of device or cabling failures, excessivebandwidth demands and various other factors that constrain “performance”as information is distributed via these shared intermediate routingnodes.

To appreciate the nature of network congestion at these sharedintermediate routing nodes, it is helpful to distinguish suchintermediate routing nodes from “user” nodes that are responsible for“consuming” digital content (i.e., destination nodes) or generating orinserting digital content onto the network (i.e., source nodes) inconnection with an application. While the network printer referencedabove (a destination node) is a shared network resource that canexperience congestion while consuming information, a much moreproblematic form of network congestion occurs at the shared intermediaterouting nodes that exist for the purpose of distributing informationacross the global Internet.

It should be noted that an Individual network node can perform thefunctionality of both a source node and a destination node. Such nodesinclude computer servers as well as client nodes—e.g., desktop andlaptop computers, smartphones, televisions, streaming media boxes,sensors and various other connected devices—regardless of the particulartype of network topology by which they are interconnected (e.g., stars,rings, trees, meshes, and virtually any type of graph or other physicalor logical topology).

While all network devices can be considered network nodes of anunderlying network such as the Internet, the user nodes that participatein a particular application are often referred to as “overlay nodes”that form an “overlay network” built on top of the underlying network.In other words, from the higher-level “logical” perspective of anapplication, only the overlay nodes are included in the “overlaydistribution” of information among those overlay nodes (even thoughinformation ultimately traverses intermediate routing nodes between anygiven pair of overlay nodes).

For example, given overlay nodes A, B, and C, the path from node A tonode C could be expressed as a set of two logical overlay paths or“links”—a first link from node A to node B, and a second link from nodeB to node C. From the perspective of the underlying network, however,each logical link between a pair of overlay nodes includes one or morelower-level hops through various intermediate routing nodes, any one ormore of which may introduce significant delays (e.g., due to networkcongestion resulting from the sharing of such intermediate routing nodesamong different overlay networks implemented by other applicationservice providers).

It is therefore important to distinguish the overlay paths or set oflinks among the overlay nodes of an overlay network from the lower-levelpaths or set of hops among intermediate routing nodes that informationtraverses as a result of the determination of each link. The creator ofeach logical link does not explicitly determine the lower-level paththat information will traverse along that link. In fact, suchlower-level paths are not known in advance, even after a link isdetermined.

Instead, these lower-level paths are determined dynamically by thedistributed routing algorithms implemented within the intermediaterouting nodes themselves. Thus, the determination of each link between apair of user nodes results (for each packet of data) in one of manydifferent lower-level paths among intermediate routing nodes along thatlink, each of which may experience network congestion to a differentextent.

It should also be noted that the distribution of information inherentlyinvolves the “relaying” of that information from one node toanother—i.e., from a “parent” node to a “child” node—whether such nodesare overlay nodes or intermediate routing nodes (or connected wirelesslyor via physical cables). For example, the overlay path referenced above(from node A to node C) involves the relaying of information via node B.Similarly, the individual link between node A and node B results in therelaying of information by various intermediate routing nodes on theunderlying network along the A→B link.

As a practical matter, in order to relay information, a node firstreceives that information and then replicates it before transmitting itto other nodes—a process that inherently requires some amount of time tocomplete, and thus introduces a period of delay. This period of delaymay increase and decrease over time as a result of changing networkcongestion, including internal congestion within a node itself. Forexample, a node may introduce delay when it performs multiple internaltasks (e.g., playing a game or recalculating a spreadsheet) that placedemands on its processor(s), memory and other computing resources, whichin turn affects its ability to relay information to other nodes.Intermediate routing nodes are particularly prone to introducing delaysbecause they are routinely shared among multiple applications.

In the context of routing packets on the Internet, physical distance (orgeographic proximity) among nodes does not significantly impactperformance because packets travel near the speed of light. Averagespeed or total throughput along a path, however, is affected by thenumber of stops or roadblocks encountered along that path, or in thiscontext the number of hops encountered at intermediate routing nodesthat relay information from a source node to a destination node. Thus,two nodes can be said to be “nearby” each other (in “network proximity”)if they are only a relatively few hops apart, regardless of theirgeographic proximity.

While network proximity can be a factor in determining throughput alonga link between two user nodes, it is not determinative for a number ofreasons. For example, the source or destination node, or anyintermediate routing node along that link, may experience congestion orother problems that introduce a variable amount of delay. User nodesparticipating in multiple standalone or network applicationssimultaneously may become congested, impacting their performance inreceiving, consuming and relaying information. Delays also may resultfrom failures in the physical cables interconnecting nodes along thatlink.

As a result, network congestion (particularly at one or moreintermediate routing nodes) may significantly affect the overall traveltime or throughput between any pair of source and destination nodes. Forexample, a 6-hop path along a link between a pair of user nodes may befaster than a 4-hop path as a result of delays due to network congestionat an intermediate routing node encountered only along the 4-hop path.

In short, network congestion has many causes. As illustrated above, theperformance of any network node or pair of interconnected nodes(including user nodes as well as intermediate routing nodes) may beimpacted by network congestion—whether due to internal demand,operational delays or failures within a node, congestion resulting fromtraffic to and from other nodes, or other causes. Moreover, becausecongestion on an underlying network such as the Internet is subject to agreat deal of volatility as network traffic ebbs and flows, suchcongestion is difficult to isolate and measure at any given time, andparticularly difficult to forecast even on a near-term basis.

When a single company such as Netflix accounts for over one-third ofpeak Internet traffic, other companies that distribute digitalinformation over the Internet must somehow address the increasinglyvolatile nature of Internet congestion if they have any hope ofsatisfying their goals of reliably consistent performance (however theydefine such goals). Similarly, as mobile voice and data usage soars, thelimited availability of regulated RF spectrum is of particular concernto companies developing high-bandwidth mobile applications.

The problem of forecasting network congestion is analogous to that offorecasting traffic congestion at the intersecting junctions of sharedroads and freeways in increasingly populated areas. While existing GPSnavigation and traffic control systems measure current congestion atthese junctions and calculate alternative paths to reroute individualdrivers around such congestion, their ability to predict desirable pathsfor any particular driver is hampered by the volatile nature of trafficcongestion.

As will become apparent below, the problem of selecting from amongalternative paths (in order to reduce the negative impact of networkcongestion) does not necessarily require perfect knowledge of the natureand location of each of the many causes of such network congestion. Itis sufficient to determine the impact of network congestion on theperformance of alternative paths or components thereof (such as anindividual node or link).

Before examining how different existing overlay network architecturesapproach the problem of network congestion, it is helpful to understandhow the underlying architecture of the Internet plays a significant rolein exacerbating the problem.

B. Underlying Internet Architecture

Beginning with ARPANET (the earliest packet-switching network toimplement the Internet protocol suite, or TCP/IP), and later NSFNET, theInternet “backbone” was designed to be a redundant “network of networks”(i.e., the Internet) that afforded reliability and “resiliency” bydecentralizing control and providing alternative communication paths forinformation to reach its desired destination. Yet, with packetsfollowing different paths through shared network resources such asintermediate routing nodes, an application's ability to maintainconsistent performance remains an extremely difficult problem.

This fundamental tradeoff between the resiliency afforded bydecentralized routing control and the desire for consistent performancelies at the heart of the Internet's network congestion problem, asevidenced by the evolution of the topology of the Internet over time.This topology can perhaps best be described as a routing hierarchyencompassing multiple different types of networks.

At the core of this routing hierarchy lies a group of interconnectednetworks each of which is often referred to as an “autonomous system”(AS). As described in Wikipedia, each AS consists of a collection ofconnected IP routing prefixes (ranges of IP addresses) “under thecontrol of one or more network operators on behalf of a singleadministrative entity or domain that presents a common, clearly definedrouting policy to the Internet.” Each AS is assigned an “autonomoussystem number” (ASN) by which it is uniquely identified on the Internetfor routing purposes.

Each of these core networks is referred to herein interchangeably as anAS or an ASN. The number of these networks has grown dramatically inrecent years, from approximately 5000 fifteen years ago to over 50,000across the world today. Together, these networks can be said to form the“backbone” of the Internet in that they exist primarily to propagate orrelay substantial amounts of information among themselves and ultimatelyto various destination user nodes in virtually every country in theworld.

Because different companies own these core networks, they often enterinto “peering” agreements with one another to facilitate the routing ofInternet traffic across these networks and throughout the globalInternet. Each AS network utilizes a bank of routers (intermediaterouting nodes) often referred to as a “peering point” to control accessto another AS network, employing a routing protocol known as the “bordergateway protocol” or BGP (as distinguished from the various routingprotocols employed by “intra-AS” intermediate routing nodes). Any givenAS may employ multiple peering points to connect to one or more other ASnetworks. Interconnected AS networks may be geographically adjacent, ormay be far apart, connected via long fiber trunks spanning greatdistances (e.g., across countries and even oceans).

As a substantial portion of the network traffic on the Internet passesthrough the intersections or “junctions” of the largest of theseinterconnected AS networks, the peering points of these AS networksexperience a great deal of network congestion—not unlike trafficcongestion at the junctions of major freeways during rush hour. Itshould be noted, however, that significant network congestion alsooccurs at the intra-AS intermediate routing nodes within these networks.

In addition to providing intermediate routing nodes that performinter-AS and intra-AS routing, many AS networks also include a specialtype of intra-AS “gateway” intermediate routing node provided byentities known as “Internet Service Providers” (ISPs). These ISP gatewayintermediate routing nodes provide a gateway to the Internet for thevarious networks of user nodes that distribute and consume the digitalcontent associated with the wide variety of applications available onthe Internet. When a user node connects to the Internet via the gatewaynode provided by its ISP, that user node can be said to have a “networklocation” within the AS network containing its gateway intermediaterouting node.

AS networks that do not include such gateway nodes are often referred toas “private backbone” networks, as opposed to “public” networks thatservice (user node) customers. Many operators of large public networks(such as major ISPs) also own private backbone networks (connected totheir own public networks and/or those owned by others) to facilitatetheir routing of significant Internet traffic.

It is important to note, however, that the intermediate routing nodes(e.g., intra-AS routers, including gateway routers, and inter-AS BGProuters) provided by AS networks are not the original source or ultimatedestination of information generated by and distributed among usersource and destination nodes. These intermediate routing nodes areinstead “conduits” designed to relay substantial amounts of informationamong themselves for the ultimate purpose of distributing informationprovided by various source user nodes among assorted destination usernodes located across the Internet. These distinctions are illustrated inFIG. 1A below.

Graph 100 a of FIG. 1A illustrates an architectural view of theInternet, consisting of a set of public AS networks 110 a. Each ASnetwork (110 a-1-110 a-8) contains two sets of intermediate routingnodes, including Inter-AS Routers (BGP routers) 115 a that interconnectand relay information among those AS networks 110 a, as well as Intra-ASRouters 125 a (including gateway routers provided by ISPs) that relayinformation within each AS network 110 a.

User Nodes 130 a connect to the Internet via the subset of Intra-ASRouters 125 a known as gateway routers. User Nodes 130 a represent thesource and destination nodes that participate in (and/or provide theunderlying functionality of) the various shared applications that run ontop of the Internet. With respect to a particular application, such UserNodes 130 a can be considered overlay nodes (also referred to as“application nodes”) that make up an overlay network associated withthat particular application. This overlay network runs on top of theInternet's underlying infrastructure—i.e., the two sets of intermediaterouting nodes 115 a and 125 a within and often across various ASnetworks 110 a.

As noted above, User Nodes 130 a can be distinguished from intermediaterouting nodes 115 a and 125 a that neither consume nor provide contentas part of any such application. And, as discussed below, one type ofoverlay network architecture (“edge-based”) consists of nodes that,while not technically part of the Internet's underlying architecture,nevertheless perform a role more akin to intermediate routing nodes thanto user nodes as referenced herein.

C. Overlay Network Architectures

As discussed above, overlay networks are built on top of underlyingnetworks, such as the Internet. One purpose of overlay networks is toaddress underlying network congestion. For example, network congestionat intermediate routing nodes along a particular link between twooverlay nodes can be addressed by reconfiguring the overlay network toeffectively “route around” or bypass such congestion (e.g., by replacingthe parent or child node of that link).

As a result of underlying network congestion, many applicationsexperience interruptions and delays that negatively impact a user's“quality of experience” (QoE)—i.e., a user-centric or application-levelview of the quality of an application's performance. In a broadcastvideo application, for example, factors such as dropped frames andrebuffering events, among others, may have an effect on a user's QoE.Moreover, a drop in QoE is often due in large part to the inability ofthe underlying shared infrastructure of the Internet to deliver aconsistent “quality of service” (QoS)—i.e., a measure of performancebased on network-centric metrics, such as throughput, latency andjitter.

Whether performance is assessed at a network-centric level ofabstraction and/or at a higher (application-specific) level ofabstraction reflecting the experience of the user of an application,various attributes of nodes and links traversed by data along anyparticular path within an overlay network can be measured over time. Werefer to such attributes generally herein as metrics.

In this context, metrics also include “external” indicators of theimpact on those nodes and links resulting from other applications andevents on the underlying network (e.g., increased traffic and delays dueto the Super Bowl or other popular bandwidth-intensive events, networkoutages in a particular area of the network, etc.). Such informationmay, for example, be obtained directly by monitoring network trafficover time, or indirectly from third parties that monitor Internettraffic and occasionally build regional or global Internet “trafficmaps” revealing specific traffic patterns over time.

During any given period of time, with respect to a particular “contentitem” distributed in connection with an application, an associated“overlay network topology” can be defined as the set of overlay nodes,along with the set of links interconnecting them, over which the contentitem (or a portion thereof) is distributed and consumed. Moreover, theperformance of that overlay network topology (and its component nodesand links) can be expressed as an application-specific function of a setof metrics.

During that period of time, the performance of the current overlaynetwork topology may or may not satisfy defined “performance criteria”,which can be expressed as an application-specific set of performanceconstraints. In the event the performance criteria are not satisfied(e.g., due to underlying network congestion), one could elect toreconfigure the overlay network topology by changing one or more of itslinks, which in turn will result in changes to the lower-level pathsthat the content item will traverse (potentially bypassing networkcongestion).

While there exist many different approaches to address the problem ofnetwork congestion on the Internet, these approaches can broadly becategorized into two distinct types of overlay network architectures.

1. Edge-Based Overlay Networks

One such architecture comprises what are referred to herein as“edge-based” overlay networks, which involve the use of additionaldedicated hardware (known as edge routers or edge servers—usedinterchangeably herein) distinct from the user nodes that originate andconsume application content. In other words, user (source anddestination) nodes are not considered part of an edge-based overlaynetwork. Instead, the overlay nodes are the edge servers themselves (asa group often referred to as a “content delivery network” or CDN).

Applications may utilize the edge servers of a CDN to providealternative “CDN paths” along any given link (from a source user node toa destination user node) for the purpose of addressing networkcongestion at the lower-level intermediate routing nodes along thatlink. However, as will become apparent below, these alternative CDNpaths address network congestion only with respect to links that passthrough the CDN.

Given that user nodes of an application may be widely dispersed on theInternet, and that network congestion can occur virtually anywhere, theedge servers of a CDN are typically located strategically at the “edges”of AS networks throughout the Internet, thereby facilitating alternativeCDN paths to user nodes “close to” (in network proximity of) one or moreof the edge servers provided by the CDN. Moreover, a CDN frequently“caches” content at those edge servers in order to reduce the number oflinks required along overlapping CDN paths.

For example, graph 100 b of FIG. 1B illustrates an architectural view ofan edge-based overlay network—shown running on top of the underlyingarchitecture of the Internet, including individual AS networks 110 b-1,110 b-2 and 110 b-3. For simplicity, the Inter-AS and Intra-ASintermediate router nodes within each AS network 110 b are not shown ingraph 100 b.

Node 120 b (in AS network 110 b-1) represents a “source” node from whicha content item originates and is distributed (via the CDNinfrastructure) to various User Nodes 130 b throughout the Internet. Inthe context of a particular application that utilizes (typically shared)CDN infrastructure, the application relies on functionality within theCDN to determine the CDN paths that a content item will traverse fromsource node 120 b to each User Node 130 b.

It should be noted that a content item may be divided into “segments”(i.e., component parts) before being distributed from source node 120 b(via the CDN infrastructure) to various User Nodes 130 b. In somescenarios, multiple source nodes are employed to distribute differentcontent items, or segments of an individual content item. As alluded toabove, even an individual segment of a content item may be furtherdivided into IP packets that are routed along different lower-levelpaths through various intermediate routing nodes.

In any event, because it is inefficient for source node 120 b todirectly distribute content to each distinct User Node 130 b,functionality within the CDN infrastructure of the edge-based overlaynetwork (i.e., the CDN edge-server overlay nodes 125 b) is employed torelay content from source node 120 b to the User Nodes 130 b via thoseoverlay nodes 125 b. In other words, with respect to each destinationUser Node 130 b (such as destination User Node 130 b-DEST), the CDNdetermines a CDN path to that destination User Node 130 b-DEST thatconsists of a set of parent-child “CDN links” between pairs of theindividual CDN edge-server overlay nodes 125 b (labeled A through I).

For example, the dashed arrows in graph 100 b illustrate a current CDNpath from source node 120 b to one particular destination User Node 130b—i.e., node 130 b-DEST. This CDN path consists of the following 5parent-child CDN links (including source node 120 b and destination UserNode 130 b): 120 b→A, A→B, B→F, F→H and finally H→130 b-DEST.

If the CDN detects congestion along the current CDN path (e.g., due tocongestion along the B→F CDN link, including the lower-levelintermediate routing nodes along that link), then the CDN may generatean alternative CDN path to bypass that detected congestion.

For example, an alternative CDN path (illustrated by the solid arrows)consists of the following 8 parent-child CDN links (also includingsource node 120 b and destination User Node 130 b): 120 b→A, A→B, B→C,C→D, D→E, E→G, G→H and finally H→130 b-DEST. This alternative 8-link CDNpath might yield faster throughput from source node 120 b to destinationnode 130 b-DEST than does the current 5-link CDN path (e.g., because itbypasses the problematic B→F CDN link).

While a CDN may be able to detect the overall throughput of variousalternative CDN paths, it should be noted that the CDN may be unable todetect the precise cause of the resulting network congestion. Forexample, network congestion in the current CDN path might be caused bythe individual B or F overlay nodes 125 b themselves, or by a particularintermediate routing node along the problematic B→F CDN link (selectedby lower-level distributed routing algorithms along the B→F link).

In any event, the alternative CDN path may achieve greater throughputbecause it does not include (and thus bypasses) the problematic B→F CDNlink—even if the CDN functionality is “unaware” of the fact (much lessthe reason) that a particular overlay node 125 b or intermediate routingnode along the B→F link is responsible for this network congestion.

In the example above, the overall throughput of alternative CDN paths isone measure of the performance of those CDN paths. Yet, in the contextof comparing the performance among multiple CDN paths, it also serves asan indirect indicator of the impact of network congestion on suchperformance. In this example, overall throughput may be sufficient toenable the CDN to select the alternative 8-link CDN path as yieldingbetter performance than the current 5-link CDN path. In other scenarios,additional indirect indicators of the performance impact of networkcongestion (i.e., other metrics) may be employed to facilitate thiscomparison of alternative CDN paths.

Moreover, by caching content at various individual edge servers, the CDNmay generate multiple overlapping CDN paths (from a source user node)with fewer overall CDN links to certain destination User Nodes 130 b byleveraging this cached content—e.g., by leveraging the fact that contentis cached at one of the CDN edge-server overlay nodes 125 b, and thusnot requiring a set of CDN links originating at the source node 120 b.

In short, edge-based overlay networks include a set of overlay nodes(edge servers 125 b) that enable the CDN routing functionality to definealternative edge-based CDN paths (to destination User Nodes 130 b) bydefining alternative sets of CDN links between pairs of its overlaynodes 125 b (such as the alternative CDN paths illustrated by therespective sets of dashed and solid arrows in graph 100 b). However, thereliance by applications on a shared edge-based overlay network resultsin a number of disadvantages.

For example, the cost of purchasing or leasing additional physicalinfrastructure (CDN edge-server overlay nodes 125 b) may be prohibitive.Edge servers are typically expensive computer servers withhigh-bandwidth connections placed at numerous strategic locations at the“edges” of various AS networks 110 b (e.g., to accommodate large numbersof User Nodes 130 b throughout the Internet).

To make edge-based overlay networks more cost effective, edge serversare often shared among various applications provided by multipleentities. As a result, these applications must share the edge servers125 b (with one another and even among the User Nodes 130 b of anindividual application) to accommodate overlapping CDN paths to thevarious destination User Nodes 130 b. As a result, edge-based overlaynetworks may actually introduce network congestion and exacerbate thevery problem they are designed to solve.

Moreover, because edge-based overlay networks do not utilize User Nodes130 b as overlay nodes, their alternative CDN paths rely on CDNedge-server overlay nodes 125 b that do not consume content (as dodestination User Nodes 130 b). For example, content destined for anyUser Node 130 b must pass through the CDN (and its edge-server overlaynodes 125 b) in order to leverage the CDN's functionality.

In other words, the additional infrastructure (edge servers) provided bya CDN exists essentially to “route” (and not consume) content—more akinto the lower-level intermediate routing nodes that are part of theunderlying architecture of the Internet. As a result, edge-based overlaynetworks require additional computing resources to distribute contentamong User Nodes 130 b, which may itself introduce delays and otherinefficiencies.

In essence, edge-based overlay networks provide a less direct method ofaddressing network congestion than do “peer-based” overlay networks(discussed below), and actually contribute to network congestion byadding shared resources to the Internet ecosystem that are not directlyinvolved in the consumption of content.

2. Peer-Based Overlay Networks

An alternative architecture, comprising what are referred to herein as“peer-based” overlay networks, employs a significantly differentapproach from an edge-based architecture. Instead of relying onadditional infrastructure (edge servers) to distribute content todestination user nodes, a peer-based approach leverages the resources ofexisting destination user nodes (that receive and consume content) torelay content among themselves. In other words, in a peer-based overlaynetwork, the destination user nodes are the overlay nodes.

Thus, a “peer-to-peer” (P2P) approach leverages the resources ofselected destination user nodes (existing “capacity”) not only toreceive and consume content, but also to relay that content to otherdestination user nodes. We refer to these types of overlay nodes hereinas “peer nodes” (of a peer-based overlay network) because they may relaycontent to other such peer nodes. As noted above, such nodes are alsosometimes referred to as “application nodes” because they consumecontent in connection with a particular application (or individualcontent item).

Peer-based overlay networks can be implemented in many different typesof physical and logical network topologies (including stars, rings,trees, meshes and virtually any type of graph). Existing peer-basedoverlay networks have been employed for many different types ofapplications, such as file transfer, “video-on-demand” (VOD), audio andvideo streaming, live video broadcasting and various other contentdistribution applications.

For example, graph 100 c in FIG. 1C illustrates a tree-based topology inwhich content from source node 120 c is distributed directionally fromone peer node (User Node 130 c) to another, such that all User Nodes 130c (located throughout the Internet, including AS Networks 110c—individually illustrated as 110 c-1-110 c-3) ultimately receive andconsume the content. Unlike the edge-based overlay network illustratedin FIG. 1B, the overlay nodes in graph 100 c are the User Nodes 130 cthemselves—i.e., the nodes that receive and consume the content.

Moreover, many of these User Nodes 130 c also relay content to otherUser Nodes 130 c (as distinguished from “leaf nodes” that only receiveand consume content). The various parent-child links in FIG. 1C arelinks between pairs of User Nodes 130 c, unlike the links in FIG. 1Bwhich are CDN links between pairs of edge servers or other additionalinfrastructure that is not part of the application itself. By leveragingthe resources of the User Nodes 130 c themselves (peer-based overlaynodes) to relay content, a peer-based network facilitates thedistribution of that content among the destination User Nodes 130 c thatconsume that content—without requiring any additional external bandwidthor other resources such as those provided by the shared edge servers ofan edge-based overlay network (CDN).

For example, a peer-based overlay path from source node 120 c to UserNode 130 c-DEST (illustrated by the dashed arrows) comprises thefollowing three parent-child links: 120 c→A, A→B, and finally B→130c-DEST. A subset of this same peer-based overlay path (e.g., the singlelink from A→B) can also be employed to distribute content forconsumption by other User Nodes 130 c (e.g., node B) without requiringan alternative overlay path from source node 120 c, or any additionalinfrastructure beyond that of the User Nodes 130 c themselves.

The remaining User Nodes 130 c are serviced by other peer-based overlaypaths, and subset links thereof (illustrated by the solid arrows), inwhich “peer-to-peer” relays are employed to leverage the otherwiseunused resources of those individual peer User Nodes 130 c—e.g., tocache content temporarily and relay that content to other peer nodes aspart of alternative peer-based overlay paths. As a result, peer-basedoverlay networks tend to be more resilient and scalable than edge-basedoverlay networks in that their distributed nature facilitates recoveryfrom individual points of congestion (including device or cablingfailures)—e.g., by distributing content via other peer nodes in order tobypass such congestion.

The reliability and performance of peer-based networks actually improveas more nodes are added, and more and better alternative overlay pathsbecome available—as contrasted with edge-based networks in which theadditional physical infrastructure (edge servers) must be“load-balanced” to accommodate changing bandwidth demands (e.g., as usernodes are added and removed, and different types of content items aredeployed by various applications).

While peer-based overlay networks avoid the need for the expensiveadditional infrastructure inherent to edge-based architectures, existingpeer-based solutions have yet to effectively bypass underlying networkcongestion. This problem is particularly prevalent when a large numberof peer nodes attempt to access a popular content item (or even variousdifferent content items) during the same time period.

Existing peer-based overlay networks are typically reconfigured only tothe extent necessary to address the addition or removal of nodes—i.e.,to accommodate the “current” set of peer nodes. New links are created toadd new nodes to the system as well as to address “broken” linksresulting from nodes leaving the system. If a departing node is a leafnode, the link to that node is removed. But if that departing node is aparent node that previously relayed content to a child node, that childnode becomes an “orphaned” child node in need of a new parent node,which requires the creation of a new link.

It is desirable, however, to reconfigure the topology (whether by makingpartial modifications or effectively determining a new topology) notonly when peer nodes are added or removed, but also when (and ideallybefore) network congestion negatively impacts the performance of thepeer-based overlay network topology, including its individual nodes andlinks. In other words, to limit the impact of network congestion onperformance, it is desirable to reconfigure a peer-based overlay networktopology to effectively bypass detected (as well as prospective) networkcongestion while satisfying an application's performance criteria.

D. Need for a Predictive Overlay Network Architecture

But the task of determining which user nodes should relay content towhich other user nodes (in order to satisfy defined application-specificperformance criteria) is a daunting one, particularly as the number ofpermutations rises exponentially with the number of user nodes added toan overlay network. To appreciate the difficulty of this task, it isimportant to recognize, as alluded to above, that metrics collected overtime serve only as indirect indicators of the impact of networkcongestion on the performance of overlay network topologies.

In other words, metrics are not direct measurements of networkcongestion. Instead, they reflect the impact of network congestion onthe flow of network traffic. While network congestion affects theperformance of an overlay network topology (and its component nodes andlinks), it is the impact of network congestion on such performance thatdetermines whether that overlay network topology satisfies definedperformance criteria. Historical metrics provide data from which thatimpact can be measured and predicted. Existing overlay networkarchitectures have yet to address network congestion in a manner thatcorrelates metrics with the performance of alternative overlay networktopologies.

It should be noted that network congestion is but one obstacle inassessing the potential performance of an overlay network topology.Another (often overlooked) obstacle is the existence of“interdependencies” among the nodes and links of an overlay networktopology. These interdependencies exacerbate this problem—even apartfrom the effects of frequently changing network congestion.

Even assuming static network congestion, the prospective performance ofindividual nodes and links of any particular overlay network topology isdependent upon that of “upstream” nodes and links. In particular, theperformance of a link from a parent node to a child node is dependentupon the upstream performance of the link to that parent node. Forexample, if a node receives content from its parent node at a particularrate (e.g., 500 kbps), it cannot relay that content to its child node ata higher rate (e.g., 1 mbps). As discussed in greater detail below,however, it may have the “capacity” to replicate and relay contentsimultaneously to multiple child nodes (at a rate of up to 500 kbps toeach such child node).

Current peer-based approaches (as well as edge-based approaches) haveyet to address these interdependencies among the individual user nodesand links of an overlay network topology over which content items aredistributed and consumed. They also have failed to effectively addressthe problem of underlying network congestion in a manner thatfacilitates the reconfiguration of overlay network topologies—i.e., thedetermination of whether alternative overlay network topologies willsatisfy the performance criteria constraints imposed by applications onthe distribution and consumption of content items.

Existing peer-based overlay networks (like GPS navigation systems) tendto rely on geographic proximity to select peer relay nodes, and“reroute” traffic only “after the fact” in response to detected networkcongestion. Edge-based overlay networks rely on expensive externalphysical infrastructure (akin to building a network of freeways toprovide higher-speed alternative routes) that also fails to adequatelyaddress the problem of frequently changing network congestion in amanner that satisfies the performance criteria defined by variousapplications running on top of the shared infrastructure of anunderlying network such as the Internet.

There is thus a need to take such dependencies into account inevaluating alternative overlay network topologies in light of anapplication's performance criteria—whether network congestion isassessed reactively or prospectively.

In short, what is needed is a predictive overlay network architecturethat addresses frequently changing network congestion in a manner thatreflects the fact that the prospective performance of individual nodesand links is dependent upon that of upstream nodes and links, anddetermines an overlay network topology that will satisfy definedapplication-specific performance criteria—not only as nodes are addedand removed, but also when (and ideally before) network congestionnegatively impacts the performance experienced by destination usernodes.

II. SUMMARY

As noted above, it is well established that underlying networkcongestion throughout the Internet impacts the performance ofapplications distributing digital content via overlay networks. Thepresent invention provides a predictive overlay network architecturethat overcomes the deficiencies of existing approaches by addressing thefact that, during any given period of time, the performance of anoverlay network topology (and of its individual nodes and links) isdependent not only on underlying network congestion, but also on theconfiguration of the particular overlay network topology employed duringthat time (which produces upstream and downstream dependencies among thenodes and links of that overlay network topology). The present inventionreconfigures the current overlay network topology over time, not only toaccommodate nodes that have been added to and removed from the network,but also to improve performance while satisfying defined performancecriteria.

In one embodiment, the present invention addresses frequently changingnetwork congestion in part by measuring the effects of networkcongestion on the performance of individual nodes and links of anoverlay network topology. It uses such measurements to predict thecapacity of specified nodes and links of a prospective overlay networktopology to relay content. In particular, the “node-relaying capacity”of a prospective parent node reflects the node's ability to satisfy thedemand from one or more child nodes simultaneously, while the“link-relaying capacity” of a specified link reflects the link's abilityto satisfy the demand from the specified child node of that link.

For example, the performance of a link (A→B) in which parent node Arelays content to a child node B may be impacted by underlying networkcongestion, such as that caused by intermediate routing nodes along thelink from parent node A to child node B. Assuming a demand of 5 mbps bychild node B, if parent node A satisfies that demand (i.e., bydelivering content to child node B at 5 mbps), then the A→B link can besaid to have exhibited 5 mbps link-relaying performance (an indicator oflink-relaying capacity). Alternatively, if parent node A fails tosatisfy that demand (e.g., by delivering content to child node B at 3mbps), then the A→B link can be said to have exhibited 3 mbpslink-relaying performance.

It should be noted that, in the latter case, the failure of parent nodeA to satisfy the demand of a particular child node (child node B) mayresult from various factors, alone or in combination. For example, suchfailure may result from a downstream dependency, such as networkcongestion along the A→B link. It may also result from factors withinparent node A itself (e.g., node A's performance of other activities,such as playing a bandwidth-intensive game). Moreover, such failure mayresult from upstream dependencies (e.g., node A's parent node deliveringcontent to node A at 3 mbps).

To facilitate the assessment of such interdependencies among the nodesand links of a prospective overlay network topology, the presentinvention also considers the node-relaying capacity of prospectiveparent nodes. For example, in one embodiment, if parent node A currentlyrelays content to three child nodes simultaneously (along the A→B, A→Cand A→D links), then parent node A can be said to have exhibitednode-relaying performance equal to the sum of the link-relayingperformance of those three links. As discussed in greater detail below,parent node A's actual link-relaying capacity may even have been higher.

As alluded to above, because nodes can replicate and relay contentsimultaneously to multiple child nodes, a node receiving content at 5mbps may have the capacity to relay content to multiple child nodes (at5 mbps each) simultaneously. However, various factors may limit thenode-relaying capacity of a node. For example, a node's uplink speed maybe 10 mbps, preventing the node from relaying content (at 5 mbps each)to more than two child nodes simultaneously.

It should be noted, however, that a node with a 10 mbps node-relayingcapacity may not be able to relay content simultaneously (at 5 mbpseach) to any two child nodes. For example, if node A has a 10 mbpsnode-relaying capacity, but the A→B link has a 3 mbps link-relayingcapacity, then node A can still only relay content to child node B at 3mbps (e.g., due to downstream dependencies along the A→B link).

As discussed in greater detail below, the present invention relies onboth the node-relaying capacity and link-relaying capacity of nodes andlinks of a prospective overlay network topology to identify an overlaynetwork topology that satisfies defined performance criteria (such as a5 mbps demand from each destination node). In doing so, the presentinvention addresses frequently changing network congestion as well asthe interdependencies among the nodes and links of any prospectiveoverlay network topology.

In this regard, the present invention leverages the monitoring andprocessing of real-time metrics reflecting the performance of the nodesand links of the overlay network topologies along which content itemsare propagated. As alluded to above, during any given period of time,the current real-time metrics serve as indirect indicators of the impactof network congestion on the performance of the individual nodes andlinks of the current overlay network topology. Such metrics alsoindirectly reflect the result of upstream and downstream dependenciesproduced by the configuration of the current overlay network topology(as well as attributes inherent to the nodes and links themselves).

In one embodiment, a “metrics processor” processes raw metricsassociated with prospective nodes and links of an overlay networktopology and provides such processed metrics to a “prediction engine”(along with a metric “timestamp” reflecting the time period during whichsuch raw metrics were observed and collected). The prediction engineincludes a “node-relaying classifier” that predicts the node-relayingcapacity of specified prospective parent nodes and a “link-relayingclassifier” that predicts the link-relaying capacity of prospectivelinks.

The metrics processor generates, as inputs to the node-relayingclassifier, one or more “node metrics” which represent node-relayingattributes of prospective parent nodes—i.e., indicators of the abilityof a node to relay content to one or more child nodes. In oneembodiment, node metrics include CPU load, memory usage, operatingsystem, connection type, uplink and downlink speed, IP address, ISP, ASNand various other node-relaying attributes of a prospective parent node.In other embodiments, node metrics include various additional QoS andQoE metrics (e.g., dropped frames and rebuffering events, among others)to the extent they reflect (even indirectly) the node-relayingperformance of a prospective parent node. It will be apparent to thoseskilled in the art that fewer or additional metrics can be employedwithout departing from the spirit of the present invention.

In connection with the training of the node-relaying classifier, themetrics processor also generates corresponding outputs with respect to aprospective parent node (e.g., during each sample time period) thatreflect the current observed node-relaying performance of that parentnode. For example, if a parent node (node A) simultaneously delivered 5mbps to child node B and 3 mbps to child node C during a particular timeperiod, then a “training sample” with respect to parent node A wouldinclude (as inputs to the node-relaying classifier) the node metricsassociated with node A (along with a timestamp reflecting the timeperiod which those node metrics were obtained) and (as outputs to thenode-relaying classifier) the sum of the observed node-relayingperformance of node A (e.g., 8 mbps). In other embodiments, multipleoutputs are provided for each set of inputs (node metrics), and apredefined function is employed (in one embodiment) to calculate asingle value representing the parent node's current node-relayingperformance. In another embodiment (discussed in greater detail below),only a particular subset of observed training samples is provided to thenode-relaying classifier.

As described below, the node-relaying classifier is trained by comparingthe observed outputs of each selected training sample to the predictednode-relaying capacity generated by the node-relaying classifier. The“error” between these predicted and observed values are used to adjustweighted parameters over time to facilitate increasingly more accuratepredictions—as the node-relaying classifier learns the relationshipsbetween the node metrics of parent nodes and their node-relayingperformance with respect to their child nodes. In this manner, thenode-relaying classifier can predict the node-relaying capacity of aspecified prospective parent node even if that parent node does notcurrently have, or perhaps never had, any child nodes.

Similarly, the metrics processor generates, as inputs to thelink-relaying classifier, one or more “link metrics” which representlink-relaying attributes of prospective links—i.e., indicators of thelink's ability to deliver content to the child node of that link. In oneembodiment, link metrics include the roundtrip “ping” time along thelink, various QoE and QoS metrics (latency, jitter, etc.), as well asother link-relaying attributes of the link. In other embodiments, linkmetrics include relative node metrics regarding the parent and child ofthe link, such as their relative connection type, uplink and downlinkspeed, IP address, ISP, and ASN.

In connection with the training of the link-relaying classifier, themetrics processor also generates corresponding outputs with respect to agiven link (e.g., during each sample time period) that reflect thecurrent observed link-relaying performance of that link. In the examplenoted above, in which a parent node (node A) simultaneously delivered 5mbps to child node B and 3 mbps to child node C, then a training sampleoutput associated with the A→C link would be 3 mbps—reflecting thatlink's current link-relaying performance. Note that the correspondingset of link metric inputs includes link metrics associated with that A→Clink. In this example, additional training samples would include linkmetric inputs associated with the A→B link, along with correspondingoutputs relating to the current link-relaying performance of that A→Blink (5 mbps in this example). In one embodiment (discussed in greaterdetail below), only a particular subset of observed training samples isprovided to the link-relaying classifier.

The link-relaying classifier of the prediction engine is trained in asimilar manner to that of the node-relaying classifier, in that thepredicted link-relaying capacity generated by the link-relayingclassifier is compared with the observed outputs of each training sample(with the error between the two used to adjust weighted parameters overtime to facilitate increasingly more accurate predictions). Here too,the link-relaying classifier can predict the link-relaying capacity of aspecified prospective link even if the specified parent node of thatlink does not currently relay content (or never has relayed content) tothe specified child node of that link.

By learning the relationships between the node and link metrics and the“relay performance” of such nodes and links over time, the predictionengine predicts with increasing accuracy the ability of a prospectiveparent node to relay content to one or more child nodes, as well as theability of a prospective link to deliver content to the child node ofthat link—whether in the context of current or future networkcongestion.

As noted above, however, the actual performance of individual nodes andlinks is dependent upon their “placement” within the configuration of aparticular overlay network topology. The present invention takes intoaccount these performance interdependencies among the nodes and links ofalternative overlay network topologies by providing a “topologyselector” that takes as input the “local” node-relaying capacity andlink-relaying capacity of specified nodes and links and generates asoutput an overlay network topology that satisfies defined performancecriteria.

In one embodiment, the topology selector extracts the demand imposed bydestination nodes from known factors, such as the defined performancecriteria and the current overlay network (obtained from monitoring nodesas they join and leave the network). In other embodiments, such demandis predicted by the prediction engine.

Having obtained capacity and demand values for these prospective nodesand links, the topology selector calculates “excess capacity” (i.e.,excess relay capacity) of existing and prospective parent nodes andredistributes such excess capacity to satisfy unmet demand on a “global”basis throughout an overlay network topology—thereby generating anoverlay network topology that satisfies the defined performancecriteria. As discussed in greater detail below, the topology selector(in one embodiment) assesses prospective overlay network topologiesbased upon the extent to which they effectively redistribute excesscapacity to child nodes in need of a new or better parent—i.e., shiftingnetwork traffic to satisfy the performance criteria.

In one embodiment, the topology selector performs varioustransformations to achieve the shifting of network traffic and resultingredistribution of excess capacity. For example, higher-capacity nodesare shifted to higher levels of the overlay network topology, whilelower-capacity nodes are shifted to lower levels. Higher-capacity nodeswith excess capacity add child nodes, in some cases replacing parentnodes that fail to satisfy the demand of their child nodes. As discussedin greater detail below, various transformations are appliedindividually, in combination and in many different sequences to achievethe same goal—i.e., identifying an overlay network topology thatsatisfies the performance criteria.

In one embodiment, the topology selector selects any overlay networktopology that satisfies the performance criteria. In other embodiments,the topology selector determines the “optimal” topology—i.e., the onethat “best” satisfies (or, in another embodiment, comes “closest” tosatisfying) the performance criteria. As discussed in greater detailbelow, various linear, non-linear and multi-dimensional optimizationtechniques may be employed without departing from the spirit of thepresent invention.

In still other embodiments, the topology selector takes into accountcertain unintended “system-level” consequences of assessing prospectiveoverlay network topologies and/or implementing a particular selectedoverlay network topology. In other words, although the topology selectormay identify a desirable overlay network topology, its use of variousresources in performing this task may itself have negative consequences.

For example, in one embodiment, the overhead of simultaneously changingmany links from the current overlay network topology is a factor thataffects the selection of an overlay network topology. In otherembodiments, the frequency with which the current overlay networktopology is reconfigured is another factor (as the time to implement areconfiguration may itself impact network performance). In still otherembodiments, the topology selector, despite identifying a “sufficient”or “better” topology, will not replace the current overlay networktopology as a result of various tradeoffs of time, performance, memoryand other resources. As will be evident to those skilled in the art,various other tradeoffs, optimizations and other techniques may beemployed without departing from the spirit of the present invention.

Moreover, the frequency with which the topology selector performs itsassessment of prospective overlay network topologies also may vary,depending upon the particular application (or even the content item ortype of content item). In some embodiments, the “trigger” that causesthe topology selector to assess prospective overlay network topologiesis time-based and correlated with the time periods during which metricsare collected. For example, during each one-second time period, metricsare collected, and the topology selector determines which overlaynetwork topology (if any) will replace the current overlay networktopology. In other embodiments, the time periods are less frequent thanthose during which metrics are collected.

In still other embodiments, the trigger is event-based. For example, thetopology selector only assesses overlay network topologies when certainspecified performance thresholds are met, such as the performancecriteria nearing the point of no longer being satisfied by the currentoverlay network topology.

As noted above, the node-relaying capacity and link-relaying capacitypredictions generated by the prediction engine reflect the performanceimpact of “current” or “future” network congestion. In one embodiment,the prediction engine predicts node-relaying capacity and link-relayingcapacity multiple time periods into the future. For example, if metricsare collected every second, node-relaying capacity and link-relayingcapacity are predicted ten seconds into the future. In otherembodiments, such predictions are based on “current” network congestion,and node-relaying capacity and link-relaying capacity are predicted“zero” seconds into the future.

In the event the topology selector elects to replace the current overlaynetwork topology with another overlay network topology, subsequentsegments of the current content item will then be distributed inaccordance with the newly selected overlay network topology until suchtime as that “new current” overlay network topology is replaced.

Various alternative embodiments of the present invention are explainedin greater detail below, including embodiments resulting from design andengineering implementation tradeoffs—e.g., balancing better or optimalsolutions against factors such as cost, performance, time and otherresources. In one embodiment, the topology selector identifiesindividual child destination nodes in need of a new parent (based on athreshold performance ‘floor”) and then selects new parents for suchnodes, effectively reconfiguring a portion of the current overlaynetwork topology, rather than determining a “complete” replacementoverlay network topology.

In summary, the present invention provides:

1. A method for determining an overlay network topology that satisfies aset of one or more application-specific performance criteria withrespect to the distribution of one or more segments of a content itemalong the overlay network, the overlay network topology including aplurality of nodes of the overlay network and a plurality of links, eachlink logically interconnecting a pair of the plurality of nodes tofacilitate the distribution of the one or more segments between the pairof nodes along that link, the method comprising:(a) periodically measuring and processing a plurality of metrics duringsuccessive time periods, each metric reflecting, for each time period,an attribute associated with the nodes or links of a current overlaynetwork topology employed during that time period, wherein the processedmetrics reflect node-relaying attributes and link-relaying attributes ofthe current overlay network topology;(b) predicting, based upon the processed metrics associated with nodesand links of a prospective overlay network topology, the node-relayingcapacity and link-relaying capacity of those nodes and links; and(c) determining, based at least in part upon the predicted node-relayingcapacity and link-relaying capacity of those nodes and links, whetherthe prospective overlay network topology satisfies the performancecriteria.2. A method for reconfiguring overlay network topologies over whichcontent items are distributed, wherein each overlay network topologyincludes a plurality of network nodes and a plurality of linksinterconnecting the plurality of network nodes; the method comprisingthe following steps:(a) generating a plurality of metrics with respect to a current overlaynetwork topology;(b) generating, based upon the plurality of metrics, a plurality ofpredictions regarding a set of specified nodes and links; and(c) identifying a prospective overlay network topology based upon theplurality of predictions.3. The method of claim 2, wherein the prospective overlay networktopology satisfies performance criteria representing one or moreconstraints on the performance of the specified nodes and links.4. The method of claim 2, wherein each overlay network topology is apeer-based overlay network topology.5. The method of claim 2, further comprising the step of generatingviewer indicator predictions indicating whether each network node willbe part of the prospective overlay network topology.6. The method of claim 5, further comprising the step of generatingsession duration predictions indicating the duration of time duringwhich those network nodes that will be part of the prospective overlaynetwork topology will remain part of the prospective overlay networktopology.7. The method of claim 2, wherein the plurality of predictions includepredictions of the node-relaying capacity of the specified nodes and thelink-relaying capacity of the specified links.8. The method of claim 2, wherein the plurality of metrics include nodemetrics, link metrics and a timestamp during which the node metrics andlink metrics were obtained.9. An adaptive topology server that reconfigures overlay networktopologies over which content items are distributed, wherein eachoverlay network topology includes a plurality of network nodes and aplurality of links interconnecting the plurality of network nodes; theadaptive topology server comprising:(a) a metrics processor that generates a plurality of metrics withrespect to a current overlay network topology;(b) a prediction engine that generates, based upon the plurality ofmetrics, a plurality of predictions regarding a set of specified nodesand links; and(c) a topology selector that obtains from the prediction engine aplurality of predictions with respect to the set of specified nodes andlinks and identifies a prospective overlay network topology based uponthe plurality of predictions.10. The adaptive topology server of claim 9, wherein the topologyselector identifies a prospective overlay network topology thatsatisfies performance criteria representing one or more performanceconstraints.11. The adaptive topology server of claim 9, wherein each overlaynetwork topology is a peer-based overlay network topology.12. The adaptive topology server of claim 9, wherein the predictionengine generates viewer indicator predictions indicating whether eachnetwork node will be part of the prospective overlay network topology.13. The adaptive topology server of claim 12, wherein the predictionengine generates session duration predictions indicating the duration oftime during which those network nodes that will be part of theprospective overlay network topology will remain part of the prospectiveoverlay network topology.14. The adaptive topology server of claim 9, wherein the predictionengine generates the node-relaying capacity of the specified nodes andthe link-relaying capacity of the specified links.15. The adaptive topology server of claim 9, wherein the plurality ofmetrics generated by the metrics processor includes node metrics, linkmetrics and a timestamp during which the node metrics and link metricswere obtained.16. A method for distributing simultaneously a plurality of contentitems along respective overlay network topologies, wherein each overlaynetwork topology includes a plurality of network nodes and a pluralityof links interconnecting the plurality of network nodes; the methodcomprising the following steps:(a) distributing a first content item along a first overlay networktopology;(b) distributing a second content item along a second overlay networktopology, wherein the first content item and second content item aredistributed simultaneously along their respective overlay networktopologies;(c) distributing the first content item to a network node along thefirst overlay network topology, wherein the network node is included inboth the first overlay network topology and the second overlay networktopology, and wherein the network node consumes the first content item;and(d) distributing the second content item to the network node along thesecond overlay network topology, wherein the network node relays thesecond content item to another network node of the second overlaynetwork topology.

III. BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A is a graph illustrating one embodiment of the networkarchitecture of an underlying network of the present invention (i.e.,the Internet), including a set of user nodes participating in aparticular network application;

FIG. 1B is a graph illustrating one embodiment of the networkarchitecture of an edge-based overlay network (a content deliverynetwork or “CDN”) built on top of the underlying network of FIG. 1A, inwhich content is distributed to destination user nodes along CDN pathsamong the overlay nodes (edge servers) of the edge-based overlaynetwork;

FIG. 1C is a graph illustrating one embodiment of the networkarchitecture of a peer-based overlay network built on top of theunderlying network of FIG. 1A, in which content is distributed todestination user nodes along overlay paths among the overlay nodes(“peer” destination user nodes) of the peer-based overlay network;

FIG. 2A is a graph illustrating one embodiment of an overlay networktopology of a peer-based overlay network of the present invention,including peer nodes that are solely “child” destination nodes, and peernodes that are also “parent” nodes which relay content to other peernodes;

FIG. 2B is a graph illustrating one embodiment of multipleinterconnected peer-based overlay network topologies of the presentinvention, including at least one peer node that relays content of afirst overlay network to other peer nodes of that first overlay network,but does not consume such content;

FIG. 2C is a graph illustrating “before and after snapshots” of oneembodiment of a subset of a peer-based overlay network topology of thepresent invention, in which the current overlay network topology isreconfigured based in part upon monitored metrics;

FIG. 3A is a system diagram illustrating one embodiment of keyclient-server components of the present invention;

FIG. 3B is a block diagram illustrating one embodiment of keyclient-side components of a user node device of the present invention;

FIG. 3C is a block diagram illustrating one embodiment of keyserver-side components of an adaptive topology server of the presentinvention;

FIG. 4A is a diagram illustrating a high-level embodiment of the“Prediction Engine” and “Topology Selector” components of the “OverlayNetwork Topology Manager” illustrated in FIG. 3C;

FIG. 4B is a flowchart illustrating a high-level embodiment of thedynamic interaction among the “Metrics Processor,” “Prediction Engine”and “Topology Selector” components of the “Overlay Network TopologyManager” illustrated in FIG. 3C;

FIG. 5 is a system-level flowchart of one embodiment of a key process ofthe present invention which determines an overlay network topology thatsatisfies application-specific performance criteria in response tochanging metric indicators of the impact of underlying networkcongestion, as well as the interdependencies of the nodes and links ofthe current overlay network topology;

FIG. 6A is a diagram of one embodiment of the input and output nodes ofa neural network implementation of a node-relaying classifier of thepresent invention that predicts the node-relaying capacity of aspecified parent node to relay content to one or more child nodes;

FIG. 6B is a diagram of one embodiment of the input and output nodes ofa neural network implementation of a link-relaying classifier of thepresent invention that predicts the link-relaying capacity of aspecified link to relay content from the parent node of that link to thespecified child node of that link;

FIG. 7A is a graph illustrating one embodiment of the state of anoverlay network topology following an initial configuration by theTopology Selector component of the Overlay Network Topology Managerillustrated in FIG. 3C;

FIG. 7B is a graph illustrating one embodiment of the state of anoverlay network topology following a “low performance” transformation bythe Topology Selector component of the Overlay Network Topology Managerillustrated in FIG. 3C;

FIG. 7C is a graph illustrating one embodiment of the state of anoverlay network topology following a “level shifting” transformation bythe Topology Selector component of the Overlay Network Topology Managerillustrated in FIG. 3C;

FIG. 7D is a graph illustrating one embodiment of the state of anoverlay network topology following a “redistribution of excess capacity”transformation by the Topology Selector component of the Overlay NetworkTopology Manager illustrated in FIG. 3C;

FIG. 7E is a flowchart illustrating one embodiment of initialconfiguration and reconfiguration transformations performed by theTopology Selector component of the Overlay Network Topology Managerillustrated in FIG. 3C;

FIG. 7F is a flowchart illustrating an alternative embodiment of keysteps of the Topology Selector component of the Overlay Network TopologyManager illustrated in FIG. 3C, in which new, orphaned and lowperformance nodes are identified as requiring new parents in order tofacilitate a “local” reconfiguration of a current overlay networktopology.

IV. DETAILED DESCRIPTION A. Introduction

As discussed in greater detail below, the present invention is directedtoward the distribution of content items among nodes of an underlyingnetwork such as the Internet. While embodiments of the predictiveoverlay network architecture of the present invention are describedherein in the context of peer-based overlay networks built on top of theInternet, it should be emphasized that the present invention is notlimited to peer-based overlay networks, or even to the Internet. As willbecome apparent, the present invention can be integrated into edge-basedand other overlay architectures built on top of virtually any underlyingnetwork experiencing network congestion at intermediate routing nodesand other shared resources.

As alluded to above, the set of user nodes that consume the content ofan application (as distinguished from intermediate routing nodes of theunderlying network) represent overlay nodes that together define anoverlay network on which the application's content items aredistributed. For any given content item (or segment thereof), thepresent invention defines a corresponding overlay network topology,which includes the set of overlay nodes (overlay network) that consumethat content item, and the set of links (pairs of overlay nodes) alongwhich segments of the content item will propagate (until such time asthe present invention reconfigures the overlay network topology).

In one embodiment, discussed in greater detail below, one or more usernodes are part of multiple overlay networks and thus may relay, but notconsume, a particular content item. In that embodiment, however, suchuser nodes consume other content items via overlapping overlay networktopologies of which they are a part. It will be apparent to thoseskilled in the art that the scope of the present invention includessimultaneous distribution of multiple content items (each withcorresponding overlay network topologies) associated with one or moreapplications.

The embodiments of the predictive overlay network architecture of thepresent invention described below identify an overlay network topologythat satisfies a set of application-specific performance criteria.Because each application (and potentially each content item or segmentthereof) may have its own associated overlay network topology, thepresent invention may define distinct (and potentially overlapping)overlay network topologies, each of which is associated with aparticular application (or content item or segment thereof) having itsown defined performance criteria. For example, different resolutions ofa video content item may be considered distinct content items for thepurposes of the present invention.

For simplicity, however, most of the embodiments described hereindetermine a single overlay network topology associated with a singleapplication distributing segments of a single content item. It willnevertheless be apparent to those skilled in the art that any givenoverlay network topology may accommodate multiple applicationsdistributing multiple content items simultaneously, and that distinctoverlay network topologies may be defined for each application (orcontent item or segment thereof) without departing from the spirit ofthe present invention.

While many of the examples provided herein are described in the contextof delivering streaming video over the Internet to large numbers ofconcurrent users, the principles of the present invention apply equallyto virtually any type of application distributing any type of digitalcontent. Examples of applications include broadcast video, VOD, VoIP andother forms of videoconferencing, audio and video streaming, virtualreality (“VR”), single-player and multi-player gaming, large filetransfers and various other content distribution (and often relativelybandwidth-intensive) applications. Examples of digital content itemsinclude text, images, audio and/or video files, 3D models, VR gameplay,medical data and virtually any other form of digital content.

It should be further noted that the present invention is not limited tocontent items that are distributed at a scheduled time. For example,video content may be streamed live as an event occurs (whether streamedin real time or with some period of delay) or may be pre-recorded andstreamed at a later time. The event itself may or may not be scheduledin advance. Moreover, the application and its associated performancecriteria will determine whether destination nodes must receive thecontent items “simultaneously” (i.e., within a predefined thresholdperiod of time) or may receive the same content at different times.

As will become apparent below, the present invention does not “cure” theInternet's network congestion problem, or the limited capacity of thenodes and links of an overlay network to distribute content inaccordance with application-specific performance criteria. Instead, itdefines overlay network topologies over time that make efficient use ofthat limited capacity and reduce the negative impact of underlyingnetwork congestion on the performance of those overlay networktopologies (effectively reducing network congestion by “routing around”it and dispersing traffic throughout less heavily utilized or congestedareas of the Internet)—all while satisfying defined performancecriteria.

One key advantage of the present invention is the reduction of bandwidthcosts and the impact on the point-of-insertion (“POI”)—i.e., the networknode (or external network) from which the content originates. Forexample, by leveraging the destination peer nodes to deliver contentitems among themselves, the present invention avoids the need forexpensive edge-based routers and servers for distribution of contentitems. Related advantages include increased service coverage andperformance quality, even for user nodes that are well beyond the directreach of the POI (e.g., not in network proximity to the POI or perhapsto any relatively high bandwidth user node). Other advantages willbecome apparent in connection with the following description of thevarious embodiments of the present invention.

Finally, it should be emphasized that the following embodimentsrepresent allocations of functionality among hardware and softwarecomponents that are the result of various design and engineeringtradeoffs (including time, performance, memory and other factors). Thisfunctionality can be reallocated among hardware and software,client-side and server-side modules, combined into a single component orsplit among multiple components, and implemented with combinations ofstandard and custom network protocols, without departing from the spiritand scope of the present invention.

B. Peer-Based Overlay Network Topologies

Turning to FIG. 2A, graph 200 a illustrates one embodiment of apeer-based overlay network topology of the present invention. Sourcenode 220 a represents the POI or point at which the content itemoriginates and is inserted into the overlay network. In otherembodiments, content can originate from multiple different nodes,whether internal to, or external from, the overlay network itself. Anexternal “source” network or node (i.e., a node that is not part of theoverlay network and does not consume content items) may also distributecontent items to one or more peer user nodes of the overlay network.

In the embodiment illustrated in FIG. 2A, source node 220 a representsan external node that initially (per the definition of the currentoverlay network topology 200 a) distributes segments of content directlyto multiple peer nodes 230 a. Peer nodes 230 a include peer nodes thatare solely child nodes (such as “leaf” node 230 a-2, which consumescontent, but does not relay that content to any other peer node) as wellas peer nodes that are parent nodes (such as “relay” node 230 a-1, whichnot only consumes content, but also relays that content to other peernodes).

Turning to FIG. 2B, graph 200 b illustrates one embodiment of multipleoverlapping or interconnected peer-based overlay network topologies ofthe present invention. In this embodiment, a first content item isdistributed from source node 220 b-1 among a first overlay network ofmultiple peer nodes 230 b-1 that consume segments of that first contentitem. Similarly, a second content item is distributed from source node220 b-2 among a second overlay network of multiple peer nodes 230 b-2that consume segments of that second content item.

However, in this embodiment, one of the nodes of the second overlaynetwork (peer node 240 b-2) not only consumes segments of the secondcontent item and relays those segments to other peer nodes 230 b-2 ofthe second overlay network, but also relays segments of the firstcontent item to other peer nodes 230 b-1 of the first overlay network.In other words, in this embodiment, peer node 240 b-2 is an unusual node(as contrasted with other peer nodes 230 b-1 and 230 b-2) in variousrespects.

It has multiple (two) parent nodes, and it relays segments of a contentitem (the first content item) that it does not consume (since it onlyconsumes segments of the second content item). Thus, in this scenario,peer node 240 b-2 is part of multiple distinct peer-based overlaynetworks.

One purpose of this embodiment is to illustrate how the presentinvention leverages the unused or excess “relay capacity” of peer nodesthat do not consume the content being distributed—in order to “generate”a more efficient overlay network topology. It should be noted, however,that peer node 240 b-2, unlike an edge server node, does not require thepurchasing or leasing of additional physical infrastructure. Instead,peer node 240 b-2 is a user node that is already deployed to consumecontent items (of a second overlay network).

As explained in greater detail below, the present invention monitorsvarious metrics, including those involving the distribution of contentamong user nodes over time (potentially across multiple overlay networktopologies), and can thus detect (or predict) and leverage this excessrelay capacity by including node 240 b-2 in the overlay network topologyfor segments of content items distributed among peer nodes of the firstoverlay network. Variations of this concept of overlapping overlaynetwork topologies (including hybrid network architectures thatintegrate CDNs and other edge-based overlay networks) will be apparentto those skilled in the art.

Finally, it should be noted that the overlay network topologiesillustrated in FIGS. 2A and 2B represent an overlay network topologydefined by the present invention at a given point in time. In otherwords, as metrics change over time, the present invention may determinea new or modified overlay network topology to replace the currentoverlay network topology.

Turning to FIG. 2C, graph 200 c illustrates “before and after snapshots”of one embodiment of a subset of a peer-based overlay network topologyof the present invention, in which the current overlay network topology210 c is reconfigured (partially or completely) based upon monitoredmetrics, resulting in a new “replacement” overlay network topology 220 calong which future segments of a content item will propagate. Asdiscussed in greater detail below, reconfiguration may occur for avariety of reasons.

For example, metrics may change over time, indicating that theperformance of a particular node or link is (or will be) degrading.However, as alluded to above, merely replacing a “poorly performing”parent node or link may not achieve the desired result (i.e., satisfyingdefined performance criteria) without also taking into account theeffects of the interdependencies of upstream nodes and links.

Putting aside for a moment the manner in which the present inventionresolves those problems (addressing those interdependencies as well asthe effects of current or future underlying network congestion), FIG. 2Cillustrates the “before and after” effects of the reconfigurationprocess (at least on a subset of nodes and links). These effects areillustrated by the set of “before” links 215 c in current overlaynetwork topology 210 c as contrasted with the set of “after” links 225 cin reconfigured overlay network topology 220 c.

In the example illustrated in FIG. 2C, peer node X initiates a requestat some point in time to join the application and receive a particularcontent item. At that time, node X will be assigned a parent peer nodein order to receive the requested content item (though, in someembodiments discussed below, node X begins receiving content immediatelyfrom the POI—SRC node in 220 c—until a parent peer node is assigned).

As explained in greater detail below, the present invention need notassign a new parent to node X randomly, or even based solely on relativegeographic locations. Instead, it considers various metrics in selectinga parent for node X such that the performance of the resulting overlaynetwork topology as a whole (or, in some embodiments, just theperformance of the link to node X) satisfies the definedapplication-specific performance criteria. In any event, as a result ofthis process, new node X is assigned parent node A, as illustrated bythe A→X link shown in 225 c and in reconfigured overlay network topology220 c.

In addition to new nodes joining an application, the present inventionmust accommodate nodes leaving an application (in particular, parentnodes who leave “orphaned” child nodes behind). In this example, node Fleaves the application, leaving behind orphaned nodes N and O. Here too,as explained in greater detail below, the present invention considersvarious metrics in selecting new parents for those orphaned nodes. Thus,links F→N and F→O shown in 215 c (and current overlay network topology210 c) are effectively replaced by links G→N and G→O shown in 225 c andin reconfigured overlay network topology 220 c. As a result, parent nodeG now has three child nodes—orphaned nodes N and O, as well as existingchild node P.

It is important to emphasize that, even in the context of selectingparent nodes for new and orphaned nodes, the present invention considerschanging metrics to determine whether and how to reconfigure the currentoverlay network topology. In other words (as is explained in greaterdetail below), the present invention addresses the consequences offrequently changing underlying network congestion as well as theinterdependencies among nodes and links of an overlay network topology.

Thus, in addition to accommodating new and orphaned nodes, the presentinvention also addresses (observed and/or prospective) “low performance”nodes and links by reconfiguring the current overlay network topologywhile satisfying the defined performance criteria. In the exampleillustrated in FIG. 2C, the H→R and K→V links in 215 c (and currentoverlay network topology 210 c) are exhibiting (or are predicted toexhibit) “low performance”—e.g., a level of performance below apredefined threshold or below that required by the defined performancecriteria.

As alluded to above, the cause of that low performance may be aninternal problem or congestion within the nodes (node R or node V)themselves, or upstream network congestion at an intermediate routingnode along the links (H→R or K→V) to those nodes. As explained ingreater detail below, even without knowing the precise cause of theproblem, the present invention identifies an overlay network topologythat satisfies the performance criteria, and thus effectively “routesaround” and reduces underlying network congestion.

Thus, in this example, whether the cause of the “low performance”problem was existing nodes R and/or V (or existing links H→R and/orK→V), as shown in 215 c (and current overlay network topology 210 c),the present invention reconfigured current overlay network topology 210c by identifying new overlay network topology 220 c, which resulted inproviding new parent node I for child node R, and new parent node M forchild node V, as also shown in 225 c.

In some embodiments (discussed below), the present invention firstidentifies “low performance” nodes explicitly (as requiring a newparent), while in other embodiments the assignment of new parents is aresult of the identification of an overlay network topology thatsatisfies the performance criteria (without explicitly identifyingparticular “low performance” nodes).

C. Client-Server Architecture and Key Functional Components

In one embodiment of the predictive overlay network architecture of thepresent invention, a client-server architecture is employed, asillustrated in system diagram 300 a in FIG. 3A. In this embodiment,Adaptive Topology Server 310 a is a node (or multiple nodes, in otherembodiments) on the underlying network 325 a (Internet) that centralizesmuch of the functionality of the present invention.

For example, Adaptive Topology Server 310 a is responsible for managingthe one or more applications that are running simultaneously, as well asthe overlay network topologies over which information is exchanged amongUser Node devices 320 a. Each of the User Node devices 320 a is alsoconnected as an underlying node of the Internet 325 a.

Each application involves the participation of a subset of User Nodedevices 320 a, illustrated collectively as a logically interconnectedoverlay network topology 320 a-1. The “SRC” node shown in 320 a-1 is nottechnically part of the overlay network topology. It represents the POIor source of each content item. Though not otherwise shown in FIG. 3A,one or more POIs (across multiple applications and content items) aredeployed in one embodiment as external nodes on the underlying network(Internet 325 a), adapted to communicate with Adaptive Topology Server310 a, as well as with User Node Devices 320 a. In this embodiment,Adaptive Topology Server 310 a manages the distribution of the segmentsof each content item from its POI to the “root” nodes of each overlaynetwork topology 320 a-1 (at which point those segments are furtherdistributed along the overlay network topology 320 a-1 to other UserNode Devices 320 a as described in greater detail below).

In one embodiment, overlay network topology 320 a-1 is employed todistribute content with respect to multiple applications, each of whichinvolves the simultaneous distribution of one or more content items. Inother embodiments, each segment of each individual content item may bedistributed along a distinct overlay network topology.

The granularity of this correlation of an overlay network topology 320a-1 with individual segments, content items and applications is theresult of design and engineering tradeoffs made in the course ofimplementing the present invention. For simplicity, the overlay networktopology 320 a-1 is described in this context at a low level ofgranularity with reference to a subset of User Node devices 320 ainvolved in the distribution of a segment of a content item for aparticular application.

In this embodiment, User Node devices 320 a collect metrics over timeand deliver them continuously over the Internet 325 a to AdaptiveTopology Server 310, which makes decisions (based at least in part uponthose metrics) as to whether to reconfigure any particular overlaynetwork topology 320 a-1. Whenever Adaptive Topology Server 310 areconfigures a particular overlay network topology 320 a-1, itcommunicates to each parent User Node device 320 a (in that topology 320a-1) the identification of its child User Node devices 320 a to which itwill “push” subsequent segments of the current content item.

Each child User Node device 320 a includes functionality to receive andconsume segments of a content item—e.g., receiving and viewing segmentsof streamed video content, receiving and processing image files,receiving and processing interactive gameplay data, etc. If a User Nodedevice 320 a is also a parent node, it not only receives and consumessegments of a content item, but also relays those segments to theparticular User Node devices 320 a specified by Adaptive Topology Server310 a. In other words, User Node devices 320 a implement thedistribution of content over the overlay network topology 320 a-1determined by Adaptive Topology Server 310 a and reconfigured over time.

A more detailed description of the functional components in a User NodeDevice 300 b is illustrated in FIG. 3B. In one embodiment, each UserNode Device 300 b includes standard hardware and software components 310b, including CPU 312 b, memory 314 b and operating system 315 b, as wellas network adapter 316 b, for implementing the functionality ofstandalone and network applications. In other embodiments, thisfunctionality can be implemented entirely in hardware, or with the useof one or more dedicated microcontrollers rather than a general-purposeCPU 312 b and operating system 315 b, as well as with multiple (singleor multi-core) CPUs 312 b. In some embodiments, certain User NodeDevices 300 b also include I/O Devices 318 b, such as displays,keyboards, cameras, etc.

The functionality of these standard hardware and software components 310b is leveraged by the predictive overlay network architecture of thepresent invention, while also being employed for general-purpose use byUser Node Device 300 b itself. For example, Memory 314 b is alsoemployed, in some embodiments, to store custom software (e.g.,Javascript code received from Adaptive Topology Server 310 a) thatimplements certain client-side functionality of the present invention,such as collecting metrics and communicating with Adaptive TopologyServer 310 a in connection with the receipt, consumption and relaying ofsegments of content items. In other embodiments, User Node Devices 300 binclude distinct storage components for storing data and software tofacilitate this functionality.

In any event, the client-side functionality of the present invention, tothe extent not implemented in hardware, is embodied in non-transitorycomputer-accessible storage media (such as memory 314 b or other formsof data storage) and executed by a processing apparatus (such as CPU 312b). In other embodiments, this client-side functionality is embodied ina desktop application and mobile app downloaded into User Node Devices300 b.

This custom client-side functionality is also facilitated (in someembodiments) by Standard Libraries module 320 b, which includes standardprotocols and libraries for communicating with Adaptive Topology Server310 a and receiving, consuming and relaying segments of content items.Examples of such protocols and libraries include HTTP, WebSocket, STUN,WebRTC and MPEG-DASH, among others. The selection of particular standardprotocols and libraries in Standard Libraries module 320 b (as well asnon-standard protocols and libraries) is the result of various designand engineering tradeoffs within the scope of the present invention.

As alluded to above, a User Node Device 300 b may, in some embodiments,be the source of a particular content item that is distributed to otherUser Node Devices 300 b. In this scenario, Uploader 380 b implements thefunctionality of streaming or otherwise distributing each segment of thecontent item to the client User Node Devices 300 b specified by theAdaptive Topology Server 310 a. In one embodiment, Node Device 300 b, inaddition to being the source of a content item, also consumes and relayssegments of other content items (utilizing Receiver 350 b and Relayer360 b).

In this context, the resulting overlay network topology (along which anysegment of such content item is distributed) does not include that“source” User Node Device 300 b, as it is the POI or source of thecontent item. But, as noted above, that same User Node Device 300 b maybe part of a distinct (and perhaps overlapping) overlay network topologyover which a different content item is distributed (e.g., as illustratedby user node 240 b-2 in FIG. 2B discussed above).

Communications with Adaptive Topology Server 310 a are implemented byCommunicator module 330 b. For example, Communicator 330 b transmitsmetrics collected by Metrics Monitor 340 b to Adaptive Topology Server310 a—for use in determining overlay network topologies. Communicator330 b also receives from Adaptive Topology Server 310 a specificationsof the child nodes, if any, to which User Node Device 300 b will relaysubsequent segments of a content item (e.g., when Adaptive TopologyServer 310 a reconfigures an overlay network topology). In addition,Communicator 330 b handles requests by User Node Device 300 b to join orleave a particular application, among other communications-relatedfunctions.

In one embodiment, Metrics Monitor 340 b is implemented as a distributedcollector of various metrics. For example, during any given time period(e.g., every second), each User Node Device 300 b collects raw metrics,including, for example, both node metrics and link metrics, and thendelivers those metrics to Adaptive Topology Server 310 a. As discussedin greater detail below, Adaptive Topology Server 310 a organizes andprocesses the metrics it receives from all User Node Devices 300 b anduses such metrics to facilitate its determination of overlay networktopologies (across segments, content items and applications).

In alternative embodiments, User Node Devices 300 b collect metrics morefrequently than they report such metrics to Adaptive Topology Server 310a. In another embodiment, certain metrics are collected less frequently,or provided to Adaptive Topology Server 310 a only when they change. Ina further embodiment, parent nodes collect link metrics (instead of, orin addition, to relying on child nodes to collect such link metrics). Instill other embodiments, additional metrics are collected (and reportedto Adaptive Topology Server 310 a) beyond node metrics and links metrics(or even those relating directly to the transfer of segments of contentitems), such as periodic pings to known URLs and various other indirectindicators of network congestion and other changing circumstances.

As noted above, in one embodiment, node metrics include node-relayingattributes inherent to a User Node Device 300 b, such as its connectiontype (LAN, WiFi, LTE, 4G, etc.), IP address/prefix, ISP, ASN, devicetype, CPU and memory load, operating system, geographical location,uplink and downlink speeds to its gateway, etc.). Link metrics includelink-relaying attributes relating to a particular link, such asroundtrip ping times along the link, latency, jitter and othernetwork-centric metrics, and relative node metrics regarding the parentand child of the link (such as their IP address/prefix, ISP and ASN).

In other embodiments, QoE metrics (e.g., dropped frames, rebufferingevents, etc.) that reflect a user-centric or application-level view ofthe quality of an application's performance are also included asmetrics. Such QoE metrics are, of course, application-specific, and areused by Adaptive Topology Server 310 a in one embodiment (along withother metrics) to define its application-specific performance criteria.Various different or other node metrics, link metrics and other metricsmay be employed without departing from the spirit of the presentinvention.

Receiver 350 b within each User Node Device 300 b manages the protocolby which it receives segments of a content item from its parent node. Inone embodiment, standard WebRTC APIs and protocols are employed tofacilitate the peer-to-peer transmission of one or more segments of acontent item from a parent node to each of its child nodes. In otherembodiments, different standard or custom protocols are employed. Instill other embodiments, certain User Node Devices 300 b supportmultiple different protocols. The choice of protocol is a result ofdesign and engineering tradeoffs that may differ from application toapplication.

Similarly, if User Node Device 300 b is a parent node, Relayer 360 bmanages the relaying of received segments of a content item to itsspecified child nodes. Relayer 360 b is employed only when User NodeDevice 300 b has currently specified child nodes. For example, followingreconfiguration of an overlay network topology by Adaptive TopologyServer 310 a, a User Node Device 300 b may be informed that it no longerhas any specified child nodes—but may later be notified (following asubsequent reconfiguration) that it does have one or more specifiedchild nodes for distribution of subsequent segments of a content item.

Content Array Manager 370 b manages both the receipt and relaying ofsegments of a content item. For example, as segments are received,Content Array Manager 370 b buffers those segments in Receive Array 372b for use in the consumption of those segments (e.g., the viewing of abroadcast video) by Content Player 325 b in accordance with theapplication with which those segments are associated.

Content Player 325 b may, for example, be a streaming HTML5 video playerthat plays received segments of a video content item for viewing by theuser of User Node Device 300 b. If the application provides for 30 fpsplayback by Content Player 325 b, Content Array Manager 370 b maintainsa buffer of received segments (in Receive Array 372 b) which facilitatesits delivery of video frames (e.g., multiple video segments) to ContentPlayer 325 b at the appropriate rate. In some embodiments, ContentPlayer 325 b may include a distinct frame buffer to facilitate smoothplayback of a video content item.

In one embodiment, Content Player 325 b is implemented as a standardcomponent of a web browser built into (or commonly installed on) UserNode Devices 300 b—e.g., a standard Safari, Chrome or Internet Explorerweb browser. By leveraging standard functionality, the present inventionavoids the need for installing additional custom software on each UserNode Device 300 b, and thus ensures greater compatibility across usernodes. In other embodiments, Content Player 325 b is implemented as acustom web browser or standalone player.

If User Node Device 300 b is a parent node, then Content Array Manager370 b also maintains a Relay Array 374 b of received segments whichfacilitates the buffering of segments for transmission by Relayer 360 bto each child User Node Device 300 b specified by Adaptive TopologyServer 310 a. In other words, Content Array Manager 370 b maintains adistinct buffer of segments for external transmission to the Receiver350 b in each of those child User Node Devices 300 b. This buffer isemployed in other embodiments to facilitate VOD applications in which aset of child User Node Devices 300 b must receive the same segments—butat different times.

Because the Relayer 360 b within one User Node Device 300 b communicatesdirectly with the Receiver 350 b in other User Node Devices 300 b (inone embodiment), they must implement compatible protocols (such as theWebRTC APIs and protocols described above). Different User Node Devices300 b may employ different (but compatible) standard or custom protocols(or even different protocols within the Receiver 350 b and Relayer 360 bof the same User Node Device 300 b) without departing from the spirit ofthe present invention.

While the present invention (in one embodiment) leverages certainstandard functionality in User Node Device 300 b (e.g., in StandardLibraries 320 b, Content Player 325 b, and protocols implemented byReceiver 350 b and Relayer 360 b), it also relies on customfunctionality (as described above) being present on User Node Device 300b. For example, Communicator 330 b is employed to manage communicationswith Adaptive Topology Server 310 a. Metrics Monitor 340 b is employedto monitor certain metrics over time and provide them to AdaptiveTopology Server 310 a. And Receiver 350 b and Relayer 360 b are employedto manage the process of receiving segments of content items from aspecified parent node (that may change when the overlay network topologyis reconfigured). Finally, Uploader 380 b is employed to enable UserNode Device 300 b to be the source of a content item distributed alongan overlay network topology of the present invention (e.g., streaminglive or recorded video from its camera, as well as other content itemsgenerated internally or obtained from an external source).

In one embodiment, this custom functionality is downloaded by AdaptiveTopology Server 310 a to a User Node Device 300 b when it firstinitiates a request to Adaptive Topology Server 310 a to join anapplication (e.g., to view a streaming video or exchange large files).Subsequent requests to join other applications or receive other contentitems need not require that this functionality be downloaded again.

Adaptive Topology Server 310 a also communicates with the relevant POI(in one embodiment) to instruct it to provide initial segments of arequested content item to “newly joined” User Node Device 300 b untilsuch time as a parent node is selected for delivering subsequentsegments directly to User Node Device 300 b. The POI will also deliverall segments of a content item to the root nodes of each overlay networktopology 320 a-1 as discussed above. In other embodiments, in which UserNode Device 300 b is the source of a content item, Adaptive TopologyServer 310 a instructs Uploader 380 b to act as the POI in this regard(both with respect to sending initial segments to newly joined nodes andall segments to specified root nodes).

Turning to the server-side components that implement much of thefunctionality of the predictive overlay network architecture of thepresent invention, FIG. 3C illustrates one embodiment of the keycomponents of Adaptive Topology Server 300 c. As noted above, thefunctionality of Adaptive Topology Server 300 c can be implementedacross one or more physical servers, and portions of such functionalitycan be implemented entirely in hardware or in both hardware and softwareand combined into a single conceptual software module or split acrossmultiple modules (as dictated by various design and engineeringtradeoffs).

In the embodiment illustrated in FIG. 3C, Adaptive Topology Server 300 cis shown as a single physical server that includes standard hardware andsoftware components 310 c, such as CPU 312 c, memory 314 c and operatingsystem 315 c, as well as network adapter 316 c. As with User NodeDevices 300 b, this standard server-side hardware and softwarefunctionality can be implemented with the use of one or more dedicatedmicrocontrollers rather than a general-purpose CPU 312 c and operatingsystem 315 c, as well as with multiple (single or multi-core) CPUs 312c. In some embodiments, Adaptive Topology Server 300 c also includes I/ODevices 318 c, such as displays, keyboards, cameras, etc. While distinctdatabases within Adaptive Topology Server 300 c are illustrated in FIG.3C (and discussed below), memory 314 c is also employed, in someembodiments, to store custom data and functionality.

Standard Libraries 320 c are also employed in one embodiment tofacilitate communication with User Node Devices 300 b (and the variousPOI sources of content items). Here too, design and engineeringtradeoffs dictate which standard APIs and protocols are leveraged aswell as the extent to which proprietary software is deployed. As was thecase with User Node Devices 300 b, the server-side functionality of thepresent invention (to the extent not implemented in hardware) isembodied in non-transitory computer-accessible storage media (such asmemory 314 c or other forms of data storage, such as databases 375 c and385 c discussed below) and executed by a processing apparatus (such asCPU 312 c).

Signaling Server 330 c handles communications with User Node Devices 300b—e.g., for receiving metrics and instructing parent User Node Devices300 b to “push” subsequent segments of a content item to specified childnodes (without further interaction from Signaling Server 330 c). In oneembodiment, Signaling Server 330 c also facilitates the creation ofinitial “peer connections” between pairs of User Node Devices 300 b.

In another embodiment, Signaling Server 330 c is also responsible forother communications with User Node Devices 300 b. For example,Signaling Server 330 c receives requests from User Node Devices 300 b tojoin an application (and/or an individual content item). It alsomonitors “heartbeat” and other signals from User Node Devices 300 b thatindicate whether a User Node Device 300 b has lost its networkconnection or otherwise stopped viewing one or more content items, inwhich case it will be removed from the current overlay network topology.Moreover, Signaling Server 330 c handles communications with POI nodesor other sources of content in order to facilitate the streaming orother distribution of content items into the overlay network topologiesidentified by Adaptive Topology Server 300 c.

In one embodiment, Content Manager 360 c manages content items providedby multiple content providers with respect to a variety of applications.Content Manager 360 c ensures that each content item is streamed orotherwise distributed to the root nodes of the current overlay networktopology. In other words, to the extent a reconfiguration of the currentoverlay network topology (associated with a given content item) altersthose root nodes, Content Manager 360 c communicates with the relevantPOI (via Signaling Server 330 c) to ensure that the POI deliverssubsequent segments of the content item to those updated root nodes.

Content Manager 360 c also obtains or generates the application-specificperformance criteria associated with the content items of eachapplication (or, in other embodiments, with individual content items).Content Manager 360 c stores the performance criteria in Memory 314 cor, in other embodiments, in its own distinct database. As noted above,for any particular application or content item, the performance of acurrent overlay network topology (and of its individual nodes and links)is defined as a function of various metrics—and the performance criteriaare defined as a set of thresholds or other constraints imposed uponthat performance. In one embodiment, such performance criteria arepredefined for each content item. In other embodiments, the performancecriteria are generated and modified dynamically over time.

Overlay Network Topology Manager 350 c provides the major components ofthe predictive overlay network architecture of the present invention.Much of the discussion below focuses on the distribution of a particularcontent item and the reconfiguration over time of the overlay networktopology along which subsequent segments of that content item will bedistributed (following each reconfiguration). As noted above, however,the predictive overlay network architecture of the present inventionsupports the simultaneous distribution of multiple content items acrossmultiple applications.

During each defined time period, Metrics Processor 352 c receives rawmetrics primarily from the User Node Devices 300 b, but also (in oneembodiment) from external sources, whether obtained directly bymonitoring Internet traffic over time or indirectly from third partiesthat monitor Internet traffic and occasionally build regional or globalInternet “traffic maps” revealing specific traffic patterns over time.As explained in greater detail below, Metrics Processor 352 c transformsthis raw metric data into a form that can be utilized by PredictionEngine 355 c and Topology Selector 358 c to identify overlay networktopologies that satisfy application-specific performance criteria.

In one embodiment, Metrics Processor 353 organizes these raw metrics,during each successive time period, into “training samples” thatfacilitate node-relaying capacity and link-relaying capacity predictionsby Prediction Engine 355 c. For example, Metrics Processor 353quantifies the raw metrics and (in one embodiment) scales and weightsthem in order to generate training sample inputs and outputs to thenode-relaying and link-relaying classifiers.

Moreover, as explained in greater detail below, Metrics Processor 353consolidates certain metrics to generate training sample outputs to thenode-relaying classifier (e.g., combining observed metrics regarding theperformance of multiple links from a single parent node). Othertransformations of the raw metrics will be apparent to those skilled inthe art.

The metrics processed by Metrics Processor 352 c during each successivetime period (as well as other metrics obtained by Adaptive TopologyServer 300 c) are stored, in one embodiment, in Historical PerformanceDatabase 385 c. In one embodiment, these historical metrics (in both rawand processed form) are utilized by Prediction Engine 355 c.

Overlay Network Database 375 c is employed to store identifiers of thesets of nodes and links that define distinct overlay network topologies.Moreover, in another embodiment, it is employed to storeinterdependencies among the nodes and links of those overlay networktopologies and/or other data reflecting associated historical metrics.

As explained in greater detail below, Topology Selector 358 c employs,in one embodiment, non-linear multi-dimensional optimization and/orheuristic algorithms that identify an overlay network topology thatsatisfies defined application-specific performance criteria applicableto the current content item, based on specified node-relaying capacityand link-relaying capacity predictions (and, in one embodiment,predictions of demand—i.e., predictions of nodes present in the networkalong with their duration) generated by Prediction Engine 355 c.Topology Selector 358 c employs these algorithms to facilitate itsassessment of overlay network topologies based upon the extent to whichthey redistribute excess capacity to nodes in need of a new or betterparent—i.e., shifting network traffic to satisfy the performancecriteria.

Moreover, these algorithms take into account the interdependencies amongthe nodes and links in the global context of an overlay networktopology. As noted above, in the context of any particular overlaynetwork topology, the performance of each node and link is dependentupon the performance of upstream nodes and links.

In one embodiment, Topology Selector 358 c updates the nodes of thecurrent overlay network by adding newly discovered nodes and removingnodes that are no longer receiving the current content item. Moresignificantly, Topology Selector 358 c also utilizes Prediction Engine455 a to generate node-relaying capacity and link-relaying capacitypredictions for specified nodes and links, and then analyzes prospectiveoverlay network topologies including those nodes and links—while takinginterdependencies among those nodes and links into account. In anotherembodiment, additional nodes are included, even though such nodes arenot consuming the current content item (as illustrated by node 240 b-2in FIG. 2B above).

In other embodiments, Topology Selector 358 c employs algorithms toreduce the amount of time (as well as other network resources) requiredto identify an overlay network topology (and, in some embodiments, anoptimal overlay network topology) that satisfies the performancecriteria. For example, Topology Selector 358 c employs algorithms toreduce (1) the number of node-relaying capacity and link-relayingcapacity predictions it generates using Prediction Engine 455 a, and/or(2) the number of prospective overlay network topologies it assesseswith respect to the performance criteria.

In one embodiment (discussed in greater detail below with respect toFIG. 7F), Topology Selector 358 c identifies (in addition to new andorphaned nodes) a threshold number of “low performance” nodes thatrequire a new parent. This dramatically reduces the number ofnode-relaying capacity and link-relaying capacity predictions becauseonly links to such nodes (that require a new parent) need be considered.Other links will remain intact in any newly configured overlay networktopology.

In other embodiments, Topology Selector 358 c achieves additionalreductions in the number of specified node-relaying capacity andlink-relaying capacity predictions by identifying areas of the overlaynetwork topology (e.g., closer to the root or to specific “branches” orlevels of the tree) where link changes will have the greatest effect. Instill other embodiments, Topology Selector 358 c achieves similarreductions by selectively considering subsets of the number ofpermutations of prospective overlay network topologies based on thosepredictions. For example, in one such embodiment, Topology Selector 358c identifies “high performance” nodes which it utilizes as parent nodesat higher “branches” of the tree. Various other algorithms,transformations and design and engineering tradeoffs will be apparent tothose skilled in the art.

Regardless of the specific algorithms employed, Topology Selector 358 cgenerates as output an overlay network topology that satisfies theperformance criteria. As noted above, many different algorithms can beemployed without departing from the spirit of the present invention—evenif the identified overlay network topology is not the optimal one, asother factors may be prioritized (such as the time required to generatea solution).

Turning to FIG. 4A, diagram 400 a illustrates a high-level embodiment ofthe relationship between the Prediction Engine 455 a and TopologySelector 458 a components of Adaptive Topology Server 300 c. As notedabove, Prediction Engine 455 a receives as input various processedMetrics 445 a during each successive time period 453 a (in addition topreviously obtained or observed historical metrics, in one embodiment,from Historical Performance DB 485 a). Based upon these inputs,Prediction Engine 455 a generates node-relaying capacity 456 a andlink-relaying capacity 457 a predictions (and, in one embodiment, demandpredictions), which improve over time as it is trained with more diversetraining samples, as discussed in greater detail below.

In one embodiment, Topology Selector 458 a requests (from PredictionEngine 455 a) specified node-relaying capacity 456 a and link-relayingcapacity 457 a predictions. As discussed in greater detail below, itutilizes these predictions to identify an overlay network topology 460 bthat satisfies the performance criteria.

Flowchart 400 b in FIG. 4B provides a high-level dynamic illustration ofthe components of FIG. 4A. For each successive time period (illustratedby the iterative loop between Topology Selector 458 b and MetricsProcessor 452 b), Metrics Processor 452 b receives and processes rawmetric data 451 b relating to individual nodes and links. MetricsProcessor 452 b processes that raw data to generate timestamped samples453 b designed to train Prediction Engine 455 b to learn how inputnode-relaying attributes are correlated with output node-relayingperformance values (to generate node-relaying capacity predictions), andhow input link-relaying attributes are correlated with outputlink-relaying performance values (to generate link-relaying capacitypredictions).

In one embodiment, Prediction Engine 455 b (once sufficiently trained)is employed by Topology Selector 458 b to provide specifiednode-relaying capacity and link-relaying capacity predictions 456 b(and, in another embodiment, demand predictions) which facilitate theidentification by Topology Selector 458 b of an overlay network topology460 b that satisfies the performance criteria. A “training threshold” isemployed to determine when Prediction Engine 455 b is sufficientlytrained to be relied upon by Topology Selector 458 b. In anotherembodiment, Prediction Engine 455 b continuously generates node-relayingcapacity and link-relaying capacity predictions 456 b (for use byTopology Selector 458 b) which gradually improve over time.

D. Reconfiguration of Overlay Network Topologies

Flowchart 500 of FIG. 5 provides one embodiment of a slightly moredetailed system-level view of this “overlay network topologyreconfiguration process.” This process is discussed in greater detailbelow with respect to specific embodiments of key components ofPrediction Engine 455 b (in FIGS. 6A and 6B below) and key components ofTopology Selector 458 b (in FIGS. 7A-7E below).

In step 505, Content Manager 360 c defines application-specificperformance criteria with respect to each application (or, in anotherembodiment, each content item) supported by the system. With respect tothe current content item being distributed over the current overlaynetwork topology, the performance criteria represent constraints imposedupon the performance of that current overlay network topology (and ofits individual nodes and links). In one embodiment, such performance isdefined (during any specified period of time) as a function of themetrics made available to Metrics Processor 452 b—which facilitates thedetermination by Topology Selector 458 b of whether the performancecriteria are satisfied.

Metrics Processor 452 b processes the raw metrics in step 507 togenerate timestamped samples used to continually train Prediction Engine455 b. As alluded to above, given the time and resources required, itmay not be feasible for Topology Selector 458 b to reassess the state ofthe current overlay network topology during every time period in whichmetrics are collected and processed (in step 507).

Thus, Overlay Network Topology Manager 350 c performs step 510 todetermine whether to trigger this reassessment. In some embodiments,this trigger is time-based and performed with the same or with lessfrequency than the process of metrics collection. In other embodiments,the trigger is event-based. For example, in one embodiment, a thresholdperformance level is established with respect to the performance of thecurrent overlay network topology (and its individual nodes and links).If such performance is within a predefined threshold percentage offailing to satisfy the performance criteria, then step 510 triggers areassessment of the current overlay network topology beginning with step515.

Once triggered (whether via a time-based, event-based or other trigger),Topology Selector 458 b utilizes Prediction Engine 455 b in step 515 togenerate specified node-relaying capacity and link-relaying capacitypredictions. In one embodiment, such predictions are generated for eachparent node to be included in any overlay network topology considered byTopology Selector 458 b. In other embodiments, non-linear andmulti-dimensional optimization and/or heuristic algorithms are employedto reduce the number of prospective overlay network topologiesconsidered, and thus the number of required node-relaying capacity andlink-relaying capacity predictions.

Topology Selector 458 b utilizes such predictions in step 520 todetermine an overlay network topology that satisfies the performancecriteria. As noted above, in other embodiments, Topology Selector 458 bdetermines an “optimal” overlay network topology—i.e., one that bestsatisfies (or comes closest to satisfying) the performance criteria.

Once Topology Selector 458 b determines an overlay network topology thatsatisfies the performance criteria, Overlay Network Topology Manager 350c determines, in step 525, whether it will replace the current overlaynetwork topology with the one determined by Topology Selector 458 b. Asnoted above, even if a better (or an optimal) overlay network topologyexists, the overhead of changing topologies too frequently (e.g.,changing too many links at one time) may outweigh the benefit. In oneembodiment, a predefined threshold of the number of changed links isemployed to reduce this overhead. In other embodiments, a time-basedthreshold is employed (e.g., limiting the number of times the currentoverlay network topology is changed during a given period of time).Various other optimizations and techniques may be employed withoutdeparting from the spirit of the present invention.

Before turning to detailed embodiments of Prediction Engine 455 b andTopology Selector 458 b, it is helpful to recognize, as alluded toabove, that network congestion is essentially the result of demandexceeding supply. To reduce the impact of network congestion on theperformance of the current overlay network topology, Prediction Engine455 b and Topology Selector 458 b work together to reconfigure theoverlay network topology in a manner that satisfies application-specificperformance criteria, and thus reduces the extent to which demand willexceed supply (in light of current or prospective network congestion).

While Prediction Engine 455 b addresses network congestion and otherperformance-limiting factors at a local (node and link) level to predictnode-relaying capacity and link-relaying capacity, Topology Selector 458b addresses the interdependencies among the individual nodes and linksat a global (topology) level to identify an overlay network topologythat effectively redistributes excess capacity to nodes in need of a newor better parent—i.e., shifting network traffic to satisfy theperformance criteria.

1. Prediction Engine

a. Node-Relaying and Link-Relaying Classifiers

FIGS. 6A and 6B illustrate one embodiment of Prediction Engine 455 b inwhich two neural network classifiers—a node-relaying classifier 600 aand a link-relaying classifier 600 b—are employed to generate (at alocal node and link level) respective node-relaying capacity andlink-relaying capacity predictions. Node-relaying classifier 600 a inFIG. 6A generates node-relaying capacity predictions while link-relayingclassifier 600 b in FIG. 6B generates link-relaying capacitypredictions. In other embodiments, Prediction Engine 455 b generatesonly node-relaying capacity or link-relaying capacity predictions, butnot both. In still other embodiments (discussed below), PredictionEngine 455 b generates “demand predictions.”

In the embodiment illustrated in FIGS. 6A and 6B, both neural networkclassifiers 600 a and 600 b implement a form of supervised machinelearning. In other embodiments, unsupervised machine learning (e.g.,clustering of nodes based on the similarity of their various attributes)is also employed to provide additional inputs to this process. Forexample, Overlay Network Topology Manager 350 c utilizes a clusteringalgorithm to categorize the nodes of an overlay network into multipleclusters based upon multiple “dimensions” (i.e., multiple metricsincluding the metric of time, discussed in greater detail below). Byincluding an ID of the cluster in which a node is categorized as anadditional input to network classifiers 600 a and 600 b, this cluster IDfacilitates the correlation of inputs to outputs (in particular withrespect to longer-term time-dependent recurring patterns, as describedbelow).

The neural networks of the present invention are employed specificallyto correlate attributes of the nodes and links of an overlay networkwith the observed performance of such nodes and links. In oneembodiment, these neural networks correlate (over successive timeperiods) node-relaying and link-relaying attributes (e.g., input nodemetrics and link metrics) with respective node-relaying andlink-relaying performance values (reflecting the resulting performanceexperienced by child destination nodes) to facilitate respectivenode-relaying capacity and link-relaying capacity predictions.

In particular, a node-relaying classifier correlates node-relayingattributes (node metrics) with observed node-relaying performance valuesfor the purpose of predicting the “capacity” of a prospective parentnode to relay content to one or more child nodes. For example, assuminga 5 mbps demand from each child node, a predicted 13 mbps node-relayingcapacity indicates that a prospective parent node is predicted to becapable of relaying content simultaneously to (and satisfying the demandfrom) two child nodes. A predicted node-relaying capacity below 5 mbpsindicates that the specified parent node is not predicted to be capableof satisfying the demand from even a single child node, and thus shouldbe a “leaf” node.

A link-relaying classifier correlates link-relaying attributes (linkmetrics) with observed link-relaying performance values for the purposeof predicting the capacity of a prospective link—i.e., the ability ofthe link to deliver content to the child node of that link. For example,again assuming a 5 mbps demand from each child node, a predicted 5 mbpslink-relaying capacity indicates that the specified link is predicted tobe capable of delivering content to (and satisfying the demand from) thespecified child node of that link. A predicted link-relaying capacitybelow 5 mbps indicates that this link is not predicted to be capable ofsatisfying the demand of the specified child node, and thus should notbe a link in the overlay network topology.

Such correlations and “relay capacity” predictions are part of a largerprocess (described below with respect to Topology Selector 458 b) thatinvolves resolving interdependencies (among prospective nodes and linksof overlay network topologies) and redistributing excess relay capacity(to nodes in need of a new or better parent) to facilitateidentification of an overlay network topology that satisfies definedperformance criteria.

As discussed in greater detail below, Topology Selector 458 b specifiesa prospective parent node (e.g., node A) to node-relaying classifier 600a not by providing a node ID of node A, but by providing currentnode-relaying attributes (node metrics) associated with node A, fromwhich node-relaying classifier 600 a generates a predicted node-relayingcapacity value (e.g., 13 mbps) for prospective parent node A, which itdelivers back to Topology Selector 458 b.

Similarly, Topology Selector 458 b specifies a prospective link (e.g.,A→B) to link-relaying classifier 600 b not by providing a link ID of theA→B link, but by providing current link-relaying attributes (linkmetrics) associated with the A→B link, from which link-relayingclassifier 600 b generates a predicted link-relaying capacity value(e.g., 5 mbps) for the prospective A→B link, which it delivers back toTopology Selector 458 b.

b. Training of Node-Relaying and Link-Relaying Classifiers

It is important to recognize that neural network classifiers are trainedto correlate observed inputs to observed outputs so as to predictoutputs from inputs the classifiers may never have observed. In otherwords, classifiers generalize from specific observed data.

For example, if node A had never been a parent node, node-relayingclassifier 600 a would never have observed metrics relating to contenttransferred along a link from node A. Nevertheless, if Topology Selector458 b requests a node-relaying capacity prediction for node A,node-relaying classifier 600 a will still generate such a prediction. Asexplained below with respect to the details of the training process, theaccuracy of that prediction essentially depends on how similar thecurrent input metrics associated with node A are to those associatedwith other nodes (perhaps including node A) provided to node-relayingclassifier 600 a over time (i.e., observed metrics from actual parentnodes).

In other words, actual parent nodes whose attributes have been observedover time effectively serve as a “proxy” for a prospective parent nodehaving similar attributes. Both may be considered part of the same“class” of parent nodes that node-relaying classifier 600 a has learnedto correlate with node-relaying performance values experienced by thechild nodes of that class of parent nodes. Correlating multiple inputattributes to multiple output attributes is of course a relativelycomplex task, but one which is well-suited to supervised machinelearning, as will be apparent to those skilled in the art of neuralnetworks.

Similarly, if the A→K link had never been a link of any overlay networktopology, link-relaying classifier 600 b would never have observedmetrics relating to content transferred along the A→K link.Nevertheless, if Topology Selector 458 b requests a link-relayingcapacity prediction for the A→K link, link-relaying classifier 600 bwill still generate such a prediction.

Here too, the accuracy of that prediction essentially depends on howsimilar the current input link metrics associated with the A→K link areto those associated with other links (perhaps including the A→K link)provided to link-relaying classifier 600 b over time (i.e., observedmetrics from actual parent-child links). As is the case with respect tonode-relaying classifier 600 a, actual links whose attributes have beenobserved by link-relaying classifier 600 b over time effectively serveas a proxy for a prospective link having similar attributes.

Thus, in one embodiment, both node-relaying classifier 600 a andlink-relaying classifier 600 b are trained by correlating node and linkattributes with their respective node-relaying and link-relayingperformance values without regard to the specific identity of theobserved parent and child nodes.

For example, with reference to FIG. 6A, consider a training sampleprovided to node-relaying classifier 600 a with respect to parent node A(which is currently relaying content simultaneously to child nodes B andC). In this example, links A→B and A→C are part of the current overlaynetwork topology and the demand from each destination node is assumed tobe 5 mbps.

Inputs 610 a include node metrics 612 a specific to node A, such as nodeA's connection type, uplink and downlink speed, etc. Inputs 610 a alsoinclude a metric timestamp 614 a which represents the time period duringwhich the metrics for this training sample were collected (explained ingreater detail below).

The outputs 630 a of this training sample pertain to both child nodes Band C of the respective A→B and A→C links. In this example, the actualobserved performance along the A→B and A→C links (e.g., a total of 8mbps, comprised of 5 mbps along the A→B link and 3 mbps along the A→Clink) are compared to the predicted node-relaying capacity 632 a. In oneembodiment, node-relaying classifier 600 a calculates predictednode-relaying capacity 632 a (as well as actual observed performance) asa function of one or more metrics, yielding a single value. In otherembodiments, it generates multiple output values.

In one embodiment, all training samples to node-relaying classifier 600a are included, whether the actual observed performance reflects“capacity-limited” (where demand exceeds capacity) or “demand-limited”(where capacity equals or exceeds demand) observations. In otherembodiments, in an effort to more accurately predict relay capacity,training samples to node-relaying classifier 600 a are filtered toexclude demand-limited observations. In other words, because anobservation was limited by the total demand of the child nodes, it isexcluded because it may not accurately reflect the parent node's actualnode-relaying capacity.

For example, if a parent node satisfied the total demand of its one ormore child nodes (e.g., 5 mbps for one child node, 10 mbps for 2 childnodes, etc.), then that demand-limited training sample is excluded.Conversely, if the parent node failed to satisfy the demand of any ofits child nodes (as in the above example in which node A had an 8 mbpsnode-relaying capacity, but only delivered 3 mbps along the A→C link),then its capacity-limited training sample is included.

In another embodiment, certain capacity-limited training samples arealso excluded in the event that the apparent limited capacity was theresult of an upstream dependency (e.g., if the parent of node Adelivered only 3 mbps to node A) or a limitation imposed along the linkitself (such as a congested intermediate routing node along the A→Clink). In one embodiment, both of these conditions are determined byobtaining a link-relaying capacity prediction regarding the relevantlink.

Regardless of the inclusion or exclusion of particular training samples,node-relaying classifier 600 a continuously generates node-relayingcapacity predictions. In other words, it is continuously trained in thisembodiment (even though Topology Selector will not request node-relayingcapacity predictions from node-relaying classifier 600 a until it isdeemed “sufficiently” trained). The differences between predictednode-relaying capacity 632 a output values and actual observed outputvalues (not shown) represent “errors” used for training node-relayingclassifier 600 a over time (as discussed below).

Note that this training sample with respect to node A is but one of manytraining samples provided to node-relaying classifier 600 a during eachtime period. Other training samples relate of course to other nodes andlinks, as well as to the same nodes and links during successive timeperiods (including repeated submission of the same set of trainingdata).

As noted above, node-relaying classifier 600 a learns over time thecorrelation between node-relaying attributes and node-relayingperformance values. For example, if node A has a 3G cellular connectionto the Internet and delivers content to its child nodes relativelyslowly, node-relaying classifier 600 a does not specifically learn thatnode A is a “bad” parent, but instead learns more generally thatprospective parent nodes with 3G cellular connections are bad parents.This process of course is more complex as more attributes (metrics) areconsidered and their values change frequently over time.

When node-relaying classifier 600 a is employed by Topology Selector 458b to predict the node-relaying capacity 632 a of a prospective parentnode, it is supplied with inputs 610 a (current node metrics 612 a andcurrent timestamp 614 a) pertaining to a specified prospective parentnode—perhaps even one that has never been a parent node. Based on thoseinputs 610 a, node-relaying classifier 600 a generates a prediction ofthe node-relaying capacity 632 a of that specified prospective parentnode, which reflects its ability to relay content to one or more(unspecified) child nodes.

In one embodiment, one or more hidden layers 620 a are employed tofacilitate more complex correlations among multiple inputs 610 a andoutputs 630 a. In this embodiment, individual hidden neurons 621 arepresent “intermediate state” values (calculated as weighted sums orother more complex functions of the inputs to such neurons 621 a).Employing a “forward propagation” process during training, the values ofinputs 610 a are transformed through these intermediate states togenerate predicted output values, which are compared against the actualoutput values provided in each training sample.

As noted above, the differences between these generated and actualobserved output values represent “errors” in the predictions generatedby node-relaying classifier 600 a. These errors are utilized to trainnode-relaying classifier 600 a in a “back propagation” process (i.e., aform of statistical regression) that adjusts the weights used by thehidden neurons 621 a to calculate their intermediate state values. Overtime, as more representative training samples are provided,node-relaying classifier 600 a gradually reduces these errors and thusimproves its predictive capabilities. As will be apparent to thoseskilled in the art of neural networks and supervised machine learning,various different algorithms may be employed (including a single hiddenlayer or multiple “deep learning” hidden layers, as well as variousunsupervised machine learning algorithms) without departing from thespirit of the present invention.

As referenced above, metric timestamp 614 a is also included in inputs610 a, in addition to the node metrics 612 a pertaining to a specifiedparent node. During training of node-relaying classifier 600 a,timestamp 614 a represents the time period during which the metrics foreach training sample were collected. During use of node-relayingclassifier 600 a (by Topology Selector 458 b to generate a node-relayingcapacity 632 a prediction with respect to a specified prospective parentnode), timestamp 614 a represents the time period during which nodemetrics 612 a pertaining to that specified prospective parent node wereobtained.

More significantly, however, timestamp 614 a facilitates the correlationof node metrics to node-relaying performance values with respect to the“time” metric—i.e., with respect to recurring time-dependent patterns.For example, to the extent other metrics reflect patterns that recurover time (such as greater traffic delays in the evening than in themorning, or on weekends than on weekdays, or in certain areas of thecountry during inclement weather), timestamp 614 a provides valuableinformation enabling node-relaying classifier 600 a to reflect therelative effects of the time metric when used to predict node-relayingcapacity 632 a during any particular time period. In one embodiment,timestamp 614 a includes multiple values to distinguish days of the weekand time of day (whether based on a global reference time such as GMT ora local time zone) as well as holidays, special events and various othervalues instead of, or in addition to, a single precise date/time value.

Just as timestamp 614 a adds the dimension of “time” to the various nodemetrics, additional metrics are employed in other embodiments to reflectindirect factors that are “external” to the specific nodes and links ofthe current overlay network topology. For example, as noted above,external indicators of the impact on those nodes and links resultingfrom other applications and events on the underlying network are alsoincluded as inputs to Prediction Engine 455 b.

Such external indicators include periodic popular or otherbandwidth-intensive events such as the Super Bowl and season-endingepisodes of popular televisions series. These events often result inincreased traffic and delays affecting significant portions of theInternet, including the nodes and links of the current overlay networktopology. Extended network outages and equipment failures (whethercaused by inclement weather or other factors) are also included asinputs to Prediction Engine 455 b in other embodiments. As noted above,such information may be obtained directly by monitoring network trafficover time, or indirectly from third parties that monitor Internettraffic and occasionally build regional or global Internet “trafficmaps” revealing specific traffic patterns over time.

Turning to FIG. 6B, link-relaying classifier 600 b performs in a verysimilar manner to that of node-relaying classifier 600 a—with regard toits training as well as its use by Topology Selector 458 b in generatingpredictions. As noted above, however, link-relaying classifier 600 bgenerates predictions regarding the ability of a link to deliver contentsegments to a specified child node, whereas node-relaying classifier 600a generates predictions regarding the ability of a specified parent nodeto relay content segments to one or more unspecified child nodes. Whilethis distinction may appear to be a subtle one, it is quite significantin the context of Topology Selector 458 b identifying overlay networktopologies that satisfy defined performance criteria, as discussed ingreater detail below with reference to FIGS. 7A-7E.

Consider the example discussed above with reference to FIG. 6A, in whichparent node A is currently relaying content simultaneously to childnodes B and C, and thus links A→B and A→C are part of the currentoverlay network topology. Unlike the single training sample generatedfor node-relaying classifier 600 a, two training samples forlink-relaying classifier 600 b would be generated in the context of thisexample—one with respect to the A→B link and another with respect to theA→C link.

With respect to the link associated with either training sample, theinputs 610 b to link-relaying classifier 600 b include link metrics 612b, such as roundtrip ping times along the link, relative node metricsregarding the parent and child of the link, particular QoS and QoEmetrics and other link-relaying attributes. Inputs 610 b also includemetric timestamp 614 b, which represents the time period during whichthe link metrics 612 b were collected (as discussed above with respectto node-relaying classifier 600 a and node metrics 612 a).

The outputs 630 b of link-relaying classifier 600 b represent theobserved performance or predicted capacity of the single link (ascontrasted with the outputs 630 a of node-relaying classifier 600 a,which potentially reflect the simultaneous performance of multiplelinks). The training sample outputs 630 b with respect to the A→B linkin the above example equal 5 mbps, while those with respect to the A→Clink equal 3 mbps. In one embodiment (as with node-relaying classifier600 a), link-relaying classifier 600 b generates predicted link-relayingcapacity 632 b (and actual observed performance) as a function of one ormore metrics, yielding a single value. In other embodiments, itgenerates multiple output values.

As is the case with node-relaying classifier 600 a, all training samplesto link-relaying classifier 600 b are included (in one embodiment),whether the actual observed performance reflects capacity-limited (wheredemand exceeds capacity) or demand-limited (where capacity equals orexceeds demand) observations. In other embodiments, in an effort tobetter predict relay capacity, training samples to link-relayingclassifier 600 b are separated based upon whether they arecapacity-limited or demand-limited. As a result (regardless of whetherthis separation is implemented in multiple classifiers or in separatecomponents of a single classifier), when a link-relaying capacityprediction is requested with respect to a prospective link,link-relaying classifier 600 b first determines whether the predictedcapacity satisfies the demand of the child node. In one embodiment,link-relaying classifier 600 b generates only a binary (“yes” or “no”)result. In another embodiment, in the event such demand is notsatisfied, link-relaying classifier 600 b further generates a predictedcapacity (e.g., 4 mbps, 3 mbps, etc.). Depending on the performancecriteria and other factors, such a link may still be utilized byTopology Selector 458 b (e.g., if no better link is available, or if theperformance criteria imposes a 5 mbps demand on average, but not forevery individual child node).

As with node-relaying classifier 600 a, link-relaying classifier 600 bcontinuously generates predictions—in this case, predictions oflink-relaying capacity 632 b—which it compares to actual observed outputvalues to gradually reduce errors over time. It also relies on trainingsamples associated with actual observed links during each time period,and across successive time periods (including repeated submission of thesame set of training data)—obviating the need to memorize attributes ofspecific nodes and links.

When link-relaying classifier 600 b is employed by Topology Selector 458b to predict the link-relaying capacity 632 b of a prospective link, itis supplied with inputs 610 b (currently-sampled link metrics 612 b andtimestamp 614 b) pertaining to a specified prospective link—perhaps evenone that has never been part of an actual observed overlay networktopology. Based on those inputs 610 b, link-relaying classifier 600 bgenerates a prediction of the link-relaying capacity 632 b of thatspecified link, which reflects the ability of the link to delivercontent to the specified child node of that link.

As is the case with node-relaying classifier 600 a, one or more hiddenlayers 620 b are employed to facilitate more complex correlations amongmultiple inputs 610 b and outputs 630 b of link-relaying classifier 600b. Here too, individual hidden neurons 621 b represent intermediatestate values (calculated as weighted sums or other more complexfunctions of the inputs to such neurons 621 b). In this embodiment, aforward propagation process is employed during training, transformingthe values of inputs 610 b through these intermediate states to generatepredicted link-relaying capacity 632 b values that are compared againstthe actual output values provided in each training sample. A backpropagation process is employed to adjust the weights used by the hiddenneurons 621 b to calculate their intermediate state values.

Here too, timestamp 614 b represents the time period during which themetrics for each training sample were collected (including the currenttime period) during use of link-relaying classifier 600 b by TopologySelector 458 b to generate a link-relaying capacity 632 b predictionwith respect to a specified prospective link. Moreover, as withnode-relaying classifier 600 a, timestamp 614 b facilitates thecorrelation of link metrics to link-relaying performance with respect tothe time metric, and thus with respect to recurring time-dependentpatterns as described above (including the use of additional externalindicators).

In one embodiment, the node-relaying capacity 632 a of a prospectiveparent node and link-relaying capacity 632 b of a prospective link aredefined as application-specific functions of one or more metrics (e.g.,QoE metrics that best represent the user's experience). A simplefunction might include only a single throughput metric measured in mbps.

In other embodiments, node-relaying capacity 632 a and link-relayingcapacity 632 b are defined as a more complex function of multiplemetrics—potentially including any or all metrics collected or obtainedby Adaptive Topology Server 300 c. It will be apparent to those skilledin the art that the specific function employed with respect to aparticular application (or content item) is a result of design andengineering tradeoffs aimed at distinguishing the relative performanceof particular nodes and links in light of (current or future) underlyingnetwork congestion.

As noted above, however calculated and quantified, node-relayingcapacity 632 a represents the ability of a prospective parent node torelay content segments to one or more unspecified child nodes, whilelink-relaying capacity 632 b represents the ability of a prospectivelink to deliver content segments to the specified child node of thatlink.

In one embodiment, a representative set of training samples is generatedover a predetermined “historical duration” (typically a relatively longperiod of months or years). Each set of training samples is employedrepeatedly to train node-relaying classifier 600 a and link-relayingclassifier 600 b. For example, in one embodiment, the duration of eachtime period during which metrics are collected is one second, while thehistorical duration is two years. In other embodiments, an unlimitedhistorical duration period is employed.

While metrics are collected, processed and submitted as training samplesduring each one-second time period, the set of metrics obtained duringthe historical duration period is also repeatedly submitted (overmultiple “epochs” or iterations of previously submitted trainingsamples). In this manner, node-relaying classifier 600 a andlink-relaying classifier 600 b are continuously “re-trained” withrelatively more recent metrics. In one embodiment, upon receiving asufficiently diverse set of training samples during any historicalduration period, node-relaying classifier 600 a and link-relayingclassifier 600 b are deemed “sufficiently trained” to generaterespective node-relaying capacity 632 a and link-relaying capacity 632 bpredictions upon request from Topology Selector 458 b.

As alluded to above, node-relaying classifier 600 a and link-relayingclassifier 600 b generate respective node-relaying capacity 632 a andlink-relaying capacity 632 b predictions with respect to future as wellas current network congestion. In one embodiment, such predictionsreflect future network congestion as a result of node-relayingclassifier 600 a and link-relaying classifier 600 b employing trainingsample outputs that are lagged in time relative to correspondingtraining sample inputs.

For example, if the input metrics are collected at “time n,” the actualobserved output metrics submitted to the classifiers are those collectedat a later time (e.g., “time n+5” or 5 seconds later). By training theclassifiers with lagged output metrics, the subsequent node-relayingcapacity 632 a and link-relaying capacity 632 b predictions reflect theimpact of future network congestion on such predictions. In anotherembodiment, the output metrics are not lagged (i.e., lagged for 0seconds), reflecting the impact of current network congestion on thesepredictions. It will be apparent to those skilled in the art that theamount of lag employed to adequately reflect the frequency ofsignificant changes in network congestion over time isapplication-specific, and is determined through a variety of well-knownand proprietary statistical techniques.

c. Predicting Traffic Demand

In one embodiment, the demand of destination nodes is defined by theapplication (e.g., 5 mbps demand from each child node). The existence ofsuch destination nodes on the overlay network is known to AdaptiveTopology Server 300 c which monitors when such destination nodes join orleave the overlay network.

Moreover, different destination nodes may have different traffic demands(whether measured or predicted). For example, in a broadcast videoscenario, certain viewing nodes may be capable of streaming HD video,while others may be limited to SD video. Knowledge of such differingdemands facilitates the task of Topology Selector 458 b in determiningan overlay network topology that redistributes excess capacity tosatisfy such differing demands (in accordance with the definedperformance criteria).

In other embodiments, Prediction Engine 455 b is employed to predict theexistence of a particular destination node as a viewer (represented, forexample, by a binary “viewer indicator” dependent variable). In otherwords, Prediction Engine 455 b is employed to predict which viewingnodes will be part of the overlay network topology—based, for example,on the prior behavior of such viewing nodes, as reflected by variousmetrics.

Moreover, Prediction Engine 455 b is also employed to predict (fromprior behavior) the “session duration” of such viewing nodes. Forexample, in one embodiment, viewing nodes with longer session durationsare placed at higher levels of the overlay network topology to promotestability of the topology (since changes at higher levels of the overlaynetwork topology have a greater impact and result in relatively morelink changes).

Over time, destination nodes join the network and leave the network. Byemploying Prediction Engine 455 b to correlate such decisions withobserved metrics (including the amount of time that a destination nodeparticipates in the network to consume content items), it can predictwhether a particular node will be part of the network at any given time(as well as provide cumulative information predicting the total numberof nodes in the overlay network at any given time).

Distinguishing nodes that are likely to remain on the network from nodesthat frequently disconnect from the network provides significantbenefits. For example, nodes that frequently disconnect from the network(whether due to the viewer's intent or device problems) causesignificant interruptions, particularly if they are configured atrelatively higher levels of the overlay network topology. Whenever suchnodes disappear from the network, the overlay network topology must beat least partially reconfigured, resulting in “ripple” effectsdownstream from such nodes. By placing such nodes at lower levels of theoverlay network topology, such effects are reduced. Conversely, placingnodes with higher session durations at higher levels of the overlaynetwork topology provides greater stability by minimizing the frequencyof reconfigurations and resulting disruption.

Knowing in advance whether such low-duration or high-duration nodes willlikely join the network (e.g., via viewer indicator predictions) enablesadvance planning, which in turn minimizes the time required to implementreconfigurations of the overlay network topology. Moreover, in oneembodiment, a cumulative number of viewers is determined based on theviewer indicator and session duration predictions, which enablesTopology Selector 458 b to configure an overlay network topologyoptimized for the predicted number of cumulative viewers. Variousoptimizations of the overlay network topology (including use ofheuristic algorithms) based on the cumulative number of viewers, as wellas their individual or average session duration, will be apparent tothose skilled in the art.

2. Topology Selector

At a high level, Topology Selector 458 b determines an overlay networktopology that satisfies defined application-specific performancecriteria. Topology Selector 458 b employs certain key resources tofacilitate this task. In one embodiment, it employs Prediction Engine455 b to generate relay capacity predictions for specified prospectivenodes and links and relies on known demand defined by the applicationand the monitoring of nodes joining and leaving the network. In otherembodiments, Prediction Engine 455 b generates viewer indicator andsession duration predictions to facilitate the determination by TopologySelector 458 b of an overlay network that satisfies the performancecriteria.

In one embodiment, Topology Selector 458 b determines the excesscapacity (if any) of existing and prospective parent nodes for thepurpose of shifting traffic (e.g., by connecting additional nodes aschild nodes of such parent nodes) to redistribute such excess capacity.To calculate such excess capacity, Topology Selector 458 b utilizesknown or predicted traffic demand along with known or predicted relaycapacity information.

In one embodiment, Topology Selector 458 b categorizes nodes based upontheir relative relay capacity. Local node-relaying capacity 632 a andlink-relaying capacity 632 b predictions provide such relay capacityinformation, though only at a local node and link level.

For example, predicted link-relaying capacity 632 b values for the A→Band A→C links may be sufficient to indicate that node A is a suitableparent for node B or node C, but insufficient to determine whether nodeA has adequate excess relay capacity to relay content to both node B andnode C simultaneously. Topology Selector 458 b may obtain suchinformation by requesting the node-relaying capacity 632 a value fornode A from Prediction Engine 455 b.

However, Topology Selector 458 b also considers the interdependenciesamong the nodes and links of any prospective overlay network topology.For example, unless the link to node A is sufficient to satisfy thedemand from node A (e.g., 5 mbps), then node A cannot satisfy thedemands of node B or node C, despite otherwise sufficient relay capacitypredictions. Thus, while Topology Selector 458 b utilizes the localnode-relaying capacity 632 a and link-relaying capacity 632 bpredictions generated by Prediction Engine 455 b, it also performs aglobal assessment of whether any prospective overlay network topologysatisfies the defined performance criteria.

As alluded to above, even if node A currently has no child nodes, it mayhave excess capacity to relay content to one or more child nodes. Forexample, if node A (or a “proxy” node with similar attributes)historically has relayed content simultaneously to multiple child nodes,then Prediction Engine 455 b may generate a node-relaying capacity thatexceeds the current total demand of node A's child nodes (if any).

It should be noted that a prospective parent node (whether adding afirst child node or additional child nodes) may have excess capacityonly with respect to particular child nodes (e.g., due to congestionalong the links to other child nodes). Topology Selector 458 b utilizeslink-relaying capacity 632 b predictions to identify suitable childnodes in this regard.

As discussed in greater detail below, Topology Selector 458 b takes intoaccount the interdependencies of upstream nodes and links within thecontext of an overlay network topology, in addition to the impact ofnetwork congestion (at a global topology level, as well as a local nodeand link level) on the prospective performance of any given overlaynetwork topology and its component nodes and links.

In essence, Topology Selector 458 b performs the task of identifying anoverlay network topology that satisfies the performance criteria byassessing prospective overlay network topologies based upon the extentto which they redistribute excess capacity to nodes in need of a new orbetter parent—i.e., shifting network traffic to satisfy the performancecriteria. The manner by which Topology Selector 458 b implements thisfunctionality to identify an overlay network topology that satisfies thedefined performance criteria is described in greater detail below withreference to FIGS. 7A-7E (in which it performs a global topology-levelanalysis) and FIG. 7F (in which it performs a local or partialtopology-level analysis).

It should be noted that, in one embodiment, Topology Selector 458 bemploys non-linear and multi-dimensional optimization techniques togenerate an optimal overlay network topology that satisfies theperformance criteria. In other embodiments (discussed below), variousheuristic and other transformations are employed. It will be evident toone skilled in the art that any subset of these transformations can beemployed in various different sequences within the scope of the presentinvention.

Topology Selector 458 b also (in one embodiment) requests demandpredictions (including viewer indicator and session durationpredictions) from Prediction Engine 455 b in order to facilitate itsassessment of prospective overlay network topologies. For example,Topology Selector 458 b gives priority to certain “supernodes” byselecting them as prospective parent nodes as well as placing them atrelatively higher levels of the overlay network topology. Suchsupernodes include “subscriber” nodes whose users have paid for premiumservice as well as nodes (e.g., always-on set-top boxes) that have arelatively high node-relaying capacity and a relatively long predictedsession duration. As discussed in greater detail below, TopologySelector 458 b effectively balances excess capacity against sessionduration to minimize the disruption caused when nodes frequently leavethe network.

a. Global Topology-Level Analysis

Turning to FIG. 7A, graph 700 a illustrates one embodiment of the stateof an overlay network topology following an initial configuration byTopology Selector 458 b. Beginning with the POI or source node 710 a,Topology Selector 458 b over time adds nodes to the overlay network(e.g., in response to “join” requests) and removes nodes from theoverlay network (e.g., as they leave the network or otherwise becomeunresponsive), which requires some degree of reconfiguration of theoverlay network topology by adding, removing and otherwise modifyingexisting links (even apart from reconfiguration of the overlay networktopology to address performance issues and satisfy the performancecriteria, discussed in greater detail below).

Graph 700 a illustrates an initial configuration after a handful ofnodes (A-V) have been added. As noted above, the use of a peer-basedoverlay network topology enables Topology Selector 458 b to leverage theexcess capacity of the peer nodes themselves, and shift traffic byredistributing such excess capacity to otherwise capacity-limited links.

Initially, Topology Selector 458 b has little or no availableperformance data to determine how to interconnect initial nodes joiningthe network. In one embodiment, Topology Selector 458 b relies on localrelay capacity predictions to establish initial overlay networktopologies. For example, a first node A is connected to the source node710 a. But a second node B may also be connected to source node 710 a,or may be connected as a child node of node A.

Such initial decisions are not arbitrary, despite relatively littleperformance data, because they are based on known attributes of theinitial nodes supplied to Prediction Engine 455 b (such as a node'suplink speed), as well as similar attributes of “proxy” nodes and links(as discussed above). Over time, Topology Selector 458 b obtainsgradually more accurate relay capacity information (based on relaycapacity predictions from Prediction Engine 455 b) for the purpose ofidentifying nodes with excess capacity to relay content to one or morechild nodes, as illustrated in graph 700 a.

While illustrated categories 720 a include low, medium and high relaycapacities, these three categories are provided to simplify theexplanation of graph 700 a. In other embodiments, fewer or morecategories are employed. In yet another embodiment, Topology Selector458 b utilizes the node-relaying capacity 632 a of every node in theoverlay network.

Graph 700 a illustrates a 4-level overlay network topology that wasconfigured as an initial set of nodes joined the overlay network—withnodes A-E at the “highest” level 730 a (nearest source node 710 a),following by nodes F-N at the next level 740 a, nodes O-U at the thirdlevel 750 a, and finally node V at the fourth and “lowest” level 760 a.

Improvements 765 a summarize key results of this initial configurationprocess. For example, while parent node E has a “low” relay capacity,the node-relaying capacity 632 a values from Prediction Engine 455 bare, in this scenario, sufficient to satisfy the traffic demand fromnode N.

Moreover node N (having a “medium” relay capacity) is alsosimultaneously satisfying the traffic demand from child nodes T and U.Similarly, node B (having a “high” relay capacity) is simultaneouslysatisfying the traffic demand from child nodes G, H, I, J and K. Asdiscussed in greater detail below, Topology Selector 458 b determines(e.g., by analyzing local node-relaying capacity 632 a predictions) notonly whether parent nodes B and N have sufficient excess relay capacityto relay content simultaneously to multiple child nodes, but also thenumber of such child nodes and the identification of particular suitablechild nodes. For example, as noted above, a parent node such as node Nmay have sufficient excess capacity to relay content simultaneously to 2child nodes (such as nodes T and U)—but not to relay content to 2different child nodes (e.g., due to congestion along the links to suchnodes, as evidenced by lower or insufficient link-relaying capacity 632b predictions).

Despite the work performed thus far by Topology Selector 458 b ingenerating this initial overlay network topology illustrated in graph700 a, various problems remain, as illustrated in Remaining Problems 775a. It should be noted that, in one embodiment, Topology Selector 458 bdetermines whether the performance criteria are satisfied at each stageof this process before deciding whether to reconfigure the currentoverlay network topology (e.g., to improve or optimize the overlaynetwork topology, whether or not it currently satisfies the performancecriteria).

Assuming that Topology Selector 458 b addresses the Remaining Problems775 a in this scenario, it determines that node G is not currentlysatisfying the cumulative traffic demands from its four child nodes O,P, Q and R. For example, one or more of the link-relaying capacity 632 bpredictions regarding these 4 links may indicate that the traffic demandon that particular link or links is not satisfied. Similar predictionsregarding the P→V link also indicate that the traffic demand on thatlink is not satisfied. The response by Topology Selector 458 b to theseproblems is discussed below with reference to FIG. 7B.

Other more general problems include the fact that relatively lower relaycapacity nodes (e.g., nodes A, C and E) are present at higher levels ofthe overlay network topology. Upstream dependencies on relatively lowerrelay capacity nodes can result in failures to satisfy the performancecriteria that “ripple down” the levels of the overlay network topology.

Moreover, in this scenario, while the traffic demand from many nodes issatisfied by their parent nodes, the excess capacity of such parentnodes is not distributed across capacity-limited links of the currentoverlay network topology. As a result, capacity-limited problems aremore likely to occur in the future. In an ideal scenario, traffic wouldbe shifted to redistribute excess capacity to meet the demands ofcapacity-limited links while additional excess capacity remainsavailable to address similar future concerns.

The response by Topology Selector 458 b to these more general problemsis discussed below with reference to FIGS. 7C and 7D. It should be notedthat the solutions described in FIGS. 7B-7E are, in one embodiment,performed in parallel as part of a single reconfiguration process. Inother embodiments, a subset of these processes is performed in aparticular order and may be terminated at any point upon TopologySelector 458 b identifying a replacement overlay network topology thatsatisfies the performance criteria. Nevertheless, each of theseprocesses is described individually below for the purpose ofillustrating how Topology Selector 458 b improves the current overlaynetwork topology, whether as part of a process of identifying an optimaloverlay network topology or merely one that satisfies the performancecriteria.

Turning to FIG. 7B, graph 700 b illustrates the modified state of theoverlay network topology illustrated in FIG. 7A (fed from source node710 b) after Topology Selector 458 b performs certain “low performance”transformations. As noted above, Topology Selector 458 b may elect notto perform such transformations (e.g., if the performance criteria arealready satisfied), or may elect to perform any of the transformationsillustrated in FIGS. 7A-7E (or a local partial topology analysis asillustrated in FIG. 7F) in any order. It will be apparent to one skilledin the art that such design and engineering tradeoffs may be implementedwithout departing from the spirit of the present invention.

As shown in the Improvements 765 b, Topology Selector 458 b resolves thelow performance of the P→V link by assigning node V to a differentparent (node I), thus creating the I→V link. In one embodiment, node Iis selected based in part upon the node-relaying capacity 632 aprediction with respect to parent node I and the link-relaying capacity632 b prediction with respect to the I→V link. For example, node I andnode M are both “medium” capacity nodes (per legend 720 b) with no childnodes—and thus having potentially greater excess capacity. In thisscenario, the link-relaying capacity 632 b prediction with respect tothe I→V link exceeded that of the M→V link.

In other embodiments, parent node I is selected based upon its level 740b (one level higher than former parent node P's level 750 b) in aneffort to reduce latency (e.g., by reducing the number of hops). In thisembodiment, selecting a parent from an even higher level (e.g., 730 b)is considered too disruptive, as the effects of this change will “rippledown” more of the overlay network topology and thus have more of a(potentially disruptive) downstream impact. The decision to minimizethis level of disruption is but one example of the design andengineering tradeoffs made in the implementation of the functionality ofTopology Selector 458 b.

Similarly, Topology Selector 458 b disconnects node R from “overloaded”parent node G and selects new parent node J to form the J→R link. Inthis scenario, child node R was disconnected based upon its relativelylower link-relaying capacity 632 b predictions (as compared with thoseof parent node G's other child nodes—O, P and Q). Moreover, TopologySelector 458 b determined that parent node J had sufficient excesscapacity to relay content simultaneously to both node R and node S basedupon parent node J's node-relaying capacity 632 a and the link-relayingcapacity 632 b of the J→R and J→S links (among other factors).

Note that, while node M (also having a “medium” relay capacity) had nocurrent child nodes (and thus potentially had excess capacity), thenode-relaying capacity 632 a prediction (regarding node M) and thelink-relaying capacity 632 b prediction (regarding the M→R link) in thisscenario were not sufficiently high to “outscore” potential parent nodeJ (despite the fact that node J already had an existing child node S).Here too, various design and engineering tradeoffs (made to select asufficient or optimal parent node for disconnected node R) will beapparent to those skilled in the art without departing from the spiritof the present invention.

Despite these Improvements 765 b with respect to “low performance”links, Remaining Problems 775 b have yet to be addressed. In oneembodiment, if the performance criteria are satisfied, Topology Selector458 b selects the reconfigured overlay network topology illustrated ingraph 700 b as a potential replacement for the current overlay networktopology. In other embodiments, Topology Selector 458 b seeks to furtherimprove (or, in one embodiment, optimize) the overlay network topology.

For example, nodes with relatively lower relay capacities (such as nodesA, C and E) still exist at a high level 730 b of the overlay networktopology. As noted above, the downstream effects of relying on suchnodes can result in various failures to satisfy traffic demand at lowerlevels of the overlay network topology, which in turn result in failuresto satisfy the performance criteria. Moreover, in this scenario,additional capacity-limited links remain to be addressed byredistributing excess capacity from nodes such as node M and others. Themanner in which Topology Selector 458 b addresses these RemainingProblems 775 b is discussed below with reference to FIG. 7C.

Graph 700 c in FIG. 7C illustrates the modified state of the overlaynetwork topology illustrated in FIG. 7B after Topology Selector 458 bperforms certain “level shifting” transformations. As noted above, theorder and the extent of the transformations illustrated with respect toFIGS. 7A-7E (including decisions as to whether to employ non-linear andmulti-dimensional optimization in addition to or in lieu of heuristictechniques) are the result of design and engineering tradeoffs withinthe scope of the present invention.

As shown in the Improvements 765 c, Topology Selector 458 b resolves theproblem of relatively lower relay capacity nodes (per legend 720 c)existing at relatively high levels of the overlay network topology byshifting nodes A, C and E from level 730 c down to level 740 c, whileelevating nodes G, J and N up from level 740 c to level 730 c. Node B(having a high relay capacity) is still relaying content to 5 childnodes. But node B is now relaying content to nodes A and C (in additionto nodes H, I and K), as nodes G and J have been elevated to level 730c. As a result of such “level shifting” transformations, fewercapacity-limited links are likely to exist at higher levels of theoverlay network topology.

Moreover, relatively higher relay capacity nodes (such as G, J and N)now relay content to child nodes at higher levels, ultimately resultingin lower latency. For example, while node G (now at level 730 c) stillrelays content to child nodes O, P and Q (now at level 740 c), thesenodes are in closer network proximity to source node 710 c, leavingfewer nodes at the lowest level 750 c of the overlay network topology(and thus fewer overall hops). As noted above, the number of hops fromsource node 710 c is a relevant (though not determinative) factor inoverall performance.

Finally, it should be noted that node K is now categorized as having amedium relay capacity (rather than its prior low relay capacity). Thisillustrates that the relay capacity of nodes not only varies withrespect to its prospective child nodes, but also varies over time basedupon changes in performance metrics. As noted above, such changes may bethe result of various factors. For example, node K's uplink speed may beincreasing over a given time period. Or the links from node K to itsexisting child nodes may be less congested over that time period.Regardless of the reason for these changes, Topology Selector 458 badapts to such changes, as discussed below with reference to FIG. 7D.

In one embodiment, Topology Selector 458 b employs session durationpredictions to facilitate the placement of nodes at relatively higher orlower levels of the overlay network topology—i.e., trading off capacityagainst session duration. For example, the placement of a high-capacitynode with a low predicted session duration at a high level of theoverlay network topology may result in frequent and significantdisruptions whenever that node leaves the network—including additionaltime-consuming reconfigurations of the overlay network topology, whichin turn will negatively impact the ability of Adaptive Topology Server300 c to continually satisfy the performance criteria over time.

Despite the Improvements 765 c resulting from these “level shifting”transformations, there still exist Remaining Problems 775 c that haveyet to be addressed. Here too, if the performance criteria aresatisfied, Topology Selector 458 b (in one embodiment) selects thereconfigured overlay network topology illustrated in graph 700 c as apotential replacement for the current overlay network topology. In otherembodiments, Topology Selector 458 b seeks to further improve (or, inone embodiment, optimize) the overlay network topology, as illustratedbelow with reference to FIG. 7D.

Remaining Problems 775 c include the existence of capacity-limited linksthat have yet to be addressed by redistributing excess capacity fromelsewhere in the overlay network topology. For example, in thisscenario, links B→A, A→F, G→Q and C→L are still capacity-limited, asindicated by their respective link-relaying capacity 632 b predictionsobtained from Prediction Engine 455 b. The manner in which TopologySelector 458 b addresses these Remaining Problems 775 c is discussedbelow with reference to FIG. 7D.

Graph 700 d in FIG. 7D illustrates the modified state of the overlaynetwork topology illustrated in FIG. 7C after Topology Selector 458 bperforms certain “redistribution of excess capacity” transformations. Asnoted above, the order and the extent of the transformations illustratedwith respect to FIGS. 7A-7E (including decisions as to whether to employnon-linear and multi-dimensional optimization in addition to or in lieuof heuristic techniques) are the result of design and engineeringtradeoffs within the scope of the present invention.

As shown in the Improvements 765 d, Topology Selector 458 b resolves theproblems of capacity-limited links B→A, A→F, G→Q and C→L by makingvarious link changes to reassign the child nodes of such links to parentnodes with excess capacity (and also, in one embodiment, withsufficiently high session-duration predictions).

For example, Topology Selector 458 b freed up excess capacity (for thefuture) at highest level 730 d (nearest source node 710 d) bydisconnecting node A from node B (having a high relay capacity perlegend 720 d) and node Q from node G. It also disconnected node F fromthe capacity-limited A→F link and node L from the capacity-limited C→Llink.

Having previously elevated node N to level 730 d (based on an assessmentof its excess capacity), Topology Selector 458 b assigned disconnectednode F as a second child node to node N (joining child node E). Notethat node N had previously demonstrated sufficient capacity to relaycontent to multiple child nodes (T and U). As noted above, however, thatfact alone is not sufficient to demonstrate excess capacity along theN→F link. In this scenario, however, the node-relaying capacity 632 aprediction (regarding node N) and the link-relaying capacity 632 bprediction (regarding the N→F link) provided sufficient evidence of suchexcess capacity.

Moreover, Topology Selector 458 b assigned disconnected node Q as asecond child node to parent node I (having a medium relay capacity),joining child node V. It also assigned disconnected nodes A and L toparent node K (recently elevated to medium relay capacity). These parentassignments (from level 740 d to 750 d) effectively redistribute excesscapacity to various child nodes of formerly capacity-limited links.

As a result, no significant Remaining Problems 775 c exist, and TopologySelector 458 b confirmed that the performance criteria are satisfied (atleast for the present time). By freeing up excess capacity at higherlevels of the overlay network topology, Topology Selector 458 b providesoptions for addressing future capacity-limited problems at relativelyhigher levels (fewer hops from source node 710 d).

Turning to FIG. 7E, flowchart 700 e illustrates one embodiment of thevarious initial configuration and reconfiguration transformationsperformed by Topology Selector 458 b. As noted above, in otherembodiments, Topology Selector 458 b identifies an optimal overlaynetwork topology or performs a subset of these transformations in adifferent order until the performance criteria are satisfied. It will beapparent to those skilled in the art that this functionality can becombined in many different ways to satisfy the performance criteriawithout departing from the spirit of the present invention.

Beginning with step 710 e, Topology Selector 458 b identifies new andorphaned nodes. As noted above, in one embodiment, new nodes initiaterequests to Adaptive Topology Server 300 c, while orphaned nodes areidentified by Overlay Network Topology Manager 350 c when their parentsexplicitly leave the network or fail to respond for a predefinedthreshold period of time. In other embodiments, Prediction Engine 455 bgenerates viewer indicator and session duration predictions thatfacilitate this determination by Topology Selector 458 b. TopologySelector 458 b identifies these new and orphaned nodes because they arein need of new parents, wholly apart from performance-basedreconfiguration.

In addition to these new and orphaned nodes, Topology Selector 458 balso, in one embodiment, identifies “low performance” nodes (i.e., childnodes of capacity-limited links) and disconnects them from their currentparent nodes (as discussed above with reference to FIG. 7B). In thisembodiment, these new, orphaned and disconnected nodes become prioritiesfor being assigned new parents.

In step 720 e, Topology Selector 458 b determines the node-relayingcapacities 632 a of current and prospective parent nodes and ranks suchnodes accordingly (as discussed above with reference to FIG. 7A). Asnoted above, such nodes are ranked in categories in one embodiment,while in other embodiments such nodes are ranked according to theirindividual node-relaying capacities 632 a.

In step 730 e, Topology Selector 458 b performs low performancetransformations (as discussed above with reference to FIG. 7B, and belowwith reference to FIG. 7F) to assign new parents to the previouslyidentified new, orphaned and disconnected nodes. In step 740 e, TopologySelector 458 b performs level shifting transformations (as discussedabove with reference to FIG. 7C) to elevate nodes with relatively higherrelay capacities to higher levels of the overlay network topology (anddemote nodes with relatively lower relay capacities to lower levels ofthe overlay network topology). In another embodiment, Topology Selector458 b also imposes a predefined limit on the number of hops to anydestination node. As discussed above, such decisions take into accountnode and link interdependencies, as well as node-relaying capacity 632 aand link-relaying capacity 632 b predictions.

In step 750 e, Topology Selector 458 b performs excess capacityredistribution transformations (as discussed above with reference toFIG. 7D). As noted above, some excess capacity is redistributed byshifting traffic where needed, while remaining excess capacity is freedup (particularly at higher levels of the overlay network topology) toaddress future issues of limited capacity. As with step 740 e above,such decisions take into account node and link interdependencies, aswell as node-relaying capacity 632 a and link-relaying capacity 632 bpredictions.

In one embodiment, Topology Selector 458 b repeatedly performs steps 730e, 740 e and 750 e. Each of these steps is performed sequentially or, inanother embodiment, concurrently—e.g., in the context of non-linear andmulti-dimensional optimization) until the resulting overlay networktopology satisfies the performance criteria per step 775 e (or in otherembodiments until an optimal overlay network topology is generated). Instep 780 e, the resulting overlay network topology (that satisfies, orcomes closest to satisfying, the performance criteria) is selected forpotential reconfiguration of the current overlay network topology.

While FIGS. 7A-7D illustrate overlay network topologies with arelatively small number of nodes, these concepts are equally applicableto significantly larger overlay network topologies involving virtuallyany number of nodes and interconnecting links in essentially any type ofgraph. In one embodiment, Topology Selector 458 b employs PredictionEngine 455 b to obtain node-relaying capacity 632 a and link-relayingcapacity 632 b predictions, while demand is known based on the definedperformance criteria and monitored activity of viewing nodes. In anotherembodiment, Prediction Engine 455 b predicts demand (based on viewerindicator and session duration predictions) as described above. In otherembodiments, some or all of these capacity-related and demand-relatedvalues are measured, rather than predicted.

In one embodiment, once Topology Selector 458 b identifies a prospectiveoverlay network topology that satisfies the performance criteria, itstops processing and delivers that overlay network topology forpotential replacement of the current overlay network topology, asdescribed above. In other embodiments, Topology Selector 458 b assessesall prospective overlay network topologies and selects the “optimal”one. In another embodiment, the optimal topology is the one that “bestsatisfies” (or comes closest to satisfying) the performance criteria.

In other embodiments, Topology Selector 458 b limits the number ofprospective overlay network topologies by limiting the number ofprospective links for which it requests link-relaying capacity 632 bpredictions from Prediction Engine 455 b—i.e., by reducing or filteringout nodes that are least likely to be qualified parent nodes. Forexample, in one embodiment, Topology Selector 458 b selects the “lowestperforming” nodes and excludes such nodes from consideration.

In yet another embodiment, Topology Selector 458 b first obtainsnode-relaying capacity 632 a predictions from Prediction Engine 455 b,and only considers as potential parents those nodes with the highestpredicted capacity. For example, 80% of potential parent nodes areeliminated by selecting only those nodes in the top 20% of node-relayingcapacity 632 a predictions. As a result, the number of prospectivelink-relaying capacity 632 b predictions is substantially reduced, asonly those nodes in the top 20% are parents of a specified prospectivelink. It will be apparent to those skilled in the art that determinationof an appropriate number or percentage of excluded nodes and/or links isthe result of various application-specific design and engineeringtradeoffs.

In these embodiments in which nodes (and thus links) are excluded fromconsideration by Topology Selector 458 b, the excluded nodes and linksmust still be considered, as they still must receive content as part ofthe identified (reconfigured) overlay network topology. If such nodesare not currently parent nodes, their inclusion (as a leaf node) has nodownstream effects. However, if such nodes are current parent nodes,then Topology Selector 458 b performs an additional step (in oneembodiment) upon completion of the process described above. In thisadditional step, these excluded parent nodes are reassigned as “new”nodes, and their child nodes are reassigned as “orphaned” nodes.Topology Selector 458 b effectively reconfigures its selected overlaynetwork topology to integrate these new and orphaned nodes, employing anapproach described below with reference to FIG. 7F.

b. Local (Node and Link Level) Analysis

In addition to the global “topology-level” approaches described above,including those with reduced permutations of prospective overlay networktopologies and component links and nodes, Topology Selector 458 b alsoemploys local (node and link level) approaches in other embodiments,including local optimization. In one embodiment, Topology Selector 458 bselects a subset of the current overlay network topology on which itperforms the analysis described with respect to FIGS. 7A-7E above. Forexample, given the nature of upstream dependencies discussed above,changes at lower levels of a tree-based topology are less likely to havesignificant downstream impact.

In one embodiment, Topology Selector 458 b analyzes the “lower” portionof the current overlay network topology in a “bottom up” approach,rather than identifying a completely independent “new” overlay networktopology that satisfies the performance criteria. In other words,Topology Selector 458 b analyzes each “level” of the tree, beginningwith the lowest levels (nearest the “leaf” nodes). Topology Selector 458b analyzes each successively higher level of the tree until apredetermined “percentage improvement” is achieved (and the performancecriteria are met), at which point the reconfiguration processterminates.

In other embodiments, Topology Selector 458 b performs localoptimization of selected levels of the current overlay network topology,based upon “trouble areas” identified by performing periodic performanceassessments of various component areas of the current overlay networktopology. In other words, portions of the topology that exhibit“declining performance” are reconfigured but without explicit regard forthe downstream effects of such reconfiguration (which are considered bythe global approaches discussed above).

In one embodiment, illustrated in flowchart 700 f of FIG. 7F, TopologySelector 458 b employs an alternative “child-centric” (rather than“parent-centric”) approach. Rather than limiting the number of parentnodes (and thus links) to be analyzed, Topology Selector 458 bidentifies the child nodes that “require” a new parent, and thenidentifies a “sufficient” or “optimal” parent for such nodes—as opposedto holistically identifying an overlay network topology that satisfiesthe performance criteria.

In other words, only links to that subset of nodes are modified. Oncethose nodes are assigned new parent nodes, the remaining links in thecurrent overlay network topology are undisturbed (until thatreconfigured overlay network topology is reassessed).

For example, in step 710 f, Overlay Network Topology Manger 350 cidentifies three groups of peer nodes that require a new parent node.The first group includes new nodes that have requested viewing(consumption) of the content item since Topology Selector 458 b lastreassessed the current overlay network topology. The second groupincludes orphaned nodes whose parent nodes left the network or ceasedviewing or consuming the content item.

As noted above, in one embodiment, these new and orphaned nodes alsoinclude nodes that were excluded from consideration during the globalapproach described with respect to FIGS. 7A-7E above. In thisembodiment, the exclusion or filtering processes described above precedethis process described with respect to flowchart 700 f.

The third group includes “low performance” nodes—i.e., nodes whoseperformance either fails to satisfy the defined performance criteria orfalls below a threshold level of performance and is thus deemed to be indanger of failing to satisfy the performance criteria in the nearfuture. In one embodiment, a threshold performance level is determinedbased upon node-relaying capacity 632 a predictions obtained withrespect to the parent node of a prospective “low performance” node. Forexample, those nodes whose parent node has a predicted value below athreshold performance level are considered “low performance” nodes.

In one embodiment, a maximum number (or ceiling) of low performancenodes is identified during each time period. In another embodiment, thethreshold performance level is variable, based on a floor (as well as aceiling) of low performance nodes.

Once these “target” new, orphaned and low performance nodes have beenidentified as requiring a new parent node, Topology Selector 458 brequests, in step 720 f, node-relaying capacity 632 a predictions fromPrediction Engine 455 b. Because node-relaying capacity 632 apredictions require specification only of node metrics associated withthe parent node, step 720 f is performed only once (in this embodiment)for each prospective parent node because this same node-relayingcapacity 632 a prediction apples to all target child nodes.

In one embodiment, node-relaying capacity 632 a predictions arerequested for all nodes consuming the content item, as all such nodesare prospective parents of any given target node. In other embodiments,node-relaying capacity 632 a predictions are requested for only a subsetof prospective parent nodes (e.g., based upon historical “bad parent”metrics as described above).

Having obtained all relevant node-relaying capacity 632 a predictions,step 730 f initiates the process (repeated for each target node) ofidentifying a “suitable” parent for the target node. Topology Selector458 b requests from Prediction Engine 455 b, in step 740 f,link-relaying capacity 632 b predictions (and, in another embodiment,viewer indicator and session duration predictions) for each prospectivelink to the current target node being processed. In other words, foreach prospective parent node being considered (determined in step 720 fabove), a link-relaying capacity 632 b prediction is requested for thelink from that parent node to the current target node being processed.In one embodiment, certain links are excluded based upon the exclusionof the prospective parent node (of the target child node) as a “badparent,” based on the same considerations described with respect to step720 f above.

Topology Selector 458 b then determines, in step 750 f, the parent forthat current target—based on the node-relaying capacity 632 apredictions from step 720 f above and the link-relaying capacity 632 bpredictions from step 740 f above. In one embodiment, for each giventarget node, an optimal parent node is selected based upon theperformance criteria—i.e., the parent node that “best satisfies” (orcomes closest to satisfying) the performance criteria. In otherembodiments, this process is completed once any “suitable” parent nodeis identified—i.e., a parent node that satisfies the performancecriteria.

In another embodiment, if multiple parent nodes have a sufficientlink-relaying capacity 632 b to the target child node (and sufficientexcess capacity to add the target child node to its existing childnodes, if any), the parent node with the highest excess capacity isselected. In other embodiments, the parent node with the lowest (albeitsufficient) excess capacity is selected. Various other algorithms forselecting a suitable parent for a target node will be apparent to thoseskilled in the art.

If target nodes remain (per step 775 f), the process repeats from step730 f because (as noted above), node-relaying capacity 632 a predictionshave already been obtained for all prospective parent nodes (of anyprospective target child node). Once a suitable (or optimal) parent nodeis selected for all target nodes, the process ends in step 790 f.

3. Additional Embodiments

As described above with reference to FIG. 2B, a node may be employed torelay segments of a content item that it does not consume (e.g., becauseit consumes segments of another content item). The purpose for such ascenario is to leverage the unused or excess relay capacity of a peernode that is not otherwise part of the current overlay network.

An example of one such scenario in which these “external” nodes areemployed is a live video event in which multiple resolutions (e.g., 480pand 1080p versions of a video content item) are available fordistribution. In essence, the 480p version of the video is one contentitem, delivered over a first overlay network topology, while the 1080pversion of the video is a second distinct content item, delivered“simultaneously” over a second overlay network topology.

A viewer that is currently consuming 480p or 1080p content may beidentified as having excess relay capacity. Such viewers are then addedto the other overlay network topology (for relay, but not consumption,purposes), and are thus part of two distinct (though overlapping)overlay network topologies.

In this scenario, the intent is to deliver 480p content to nodes thatare incapable of consuming and/or relaying 1080p content. Such nodesform the 480p overlay network topology. But, nodes relaying 1080pcontent that are identified as having excess relay capacity serve as avaluable resource for improving the performance of the 480p overlaynetwork (i.e., by leveraging that excess relay capacity).

Another scenario in which these “external” nodes are employed involvesdevices that are otherwise idle. For example, in one embodiment, clientsoftware (illustrated in FIG. 3B) is installed in an “always on” set-topbox which is continuously connected to the Internet—but not typicallyconsuming any content item. In this scenario, such devices often haveexcess relay capacity as they are mostly idle. They are thereforeexcellent candidates to relay segments of a content item to destinationnodes of an overlay network topology in need of additional relay nodes.

Upon determining that a current overlay network topology can benefitfrom such idle nodes, Overlay Network Topology Manager 350 c informsTopology Selector 358 c of the identity of such nodes. Topology Selector358 c then adds such nodes (i.e., as “new nodes”) to that existingoverlay network topology, as described above. In this scenario, OverlayNetwork Topology Manager 350 c adds and removes such nodes based on thestatus of one or more current overlay network topologies (i.e., wheresuch idle nodes are most needed)—rather than on the whims of a user whodecides to start viewing or stop viewing a content item.

In other embodiments, a non-tree-based topology is employed, enablingnodes to receive content segments from multiple parent nodessimultaneously. In this scenario, for example, viewers of a sportingevent receive and switch among multiple different broadcasts (e.g., toswitch among different play-by-play announcers, including their localfavorites). In other embodiments of this scenario, large medical orother data files are received from multiple different sources for thepurpose of overcoming throughput limitations such as the uplink limit ofany individual source.

In another embodiment, Overlay Network Topology Manager 350 c assigns“slots” to nodes for the purpose of facilitating the assignment ofmultiple child nodes (or, in another embodiment, multiple parent nodes)to that node. For example, Overlay Network Topology Manager 350 cassigns a default fixed number of relay slots to a node based upon itsinitial metrics (e.g., connection type, uplink and downlink speeds,etc.). It then determines, based on excess capacity identified overtime, whether to increase or decrease the node's current number of relayslots. In this manner, nodes with greater excess capacity are assignedmore child nodes. In other embodiments permitting a node to havemultiple parent nodes, the same concept is employed with respect to“incoming” slots.

As noted above, the present invention can be employed with respect tovirtually any type of application involving the distribution of digitalcontent among multiple user nodes. For example, in a VOD scenario,unlike a broadcast video scenario, nodes receive segments of a contentitem at different times. In such a scenario, as noted above, the ContentArray Manager 370 b in each user node device 300 b utilizes its bufferto facilitate the storing of segments for an extended period of time(e.g., 5-10 minutes as opposed to a typical 30 seconds for broadcastvideo). As the size of this buffer is increased, more nodes becomeavailable to broadcast content that they are not consuming at thepresent time.

Rather than maintaining distinct overlay network topologies for everydifferent period of time during which a user requests the content item,Overlay Network Topology Manager 350 c tracks these disparate timeperiods and dynamically adjusts the size of the buffer allocated tovarious parent nodes. For example, if 100 users request viewing of acontent item at 100 slightly offset periods of time, Overlay NetworkTopology Manager 350 c does not maintain 100 different overlay networktopologies, as each overlay network would have a single node (or atleast a very small number of nodes).

Instead, by increasing the size of the buffer dedicated to the contentitem, the nodes effectively distribute the content along a much smallernumber of distinct (but overlapping) overlay network topologies—eachwith carefully synchronized buffer sizes to provide segments todifferent users at different times (all managed by Overlay NetworkTopology Manager 350 c). For example, in one embodiment, a 10-minutebuffer is employed to enable the distribution of a two-hour video via adozen overlapping overlay network topologies. In other embodiments,additional features (pause, rewind, etc.) are implemented by effectivelymoving nodes among different overlay network topologies.

The present invention has been described herein with reference tospecific embodiments as illustrated in the accompanying drawings. Itshould be understood that, in light of the present disclosure,additional embodiments of the concepts disclosed herein may beenvisioned and implemented within the scope of the present invention bythose skilled in the art.

1-16. (canceled)
 17. A method for determining an overlay networktopology that satisfies a set of one or more application-specificperformance criteria with respect to the distribution of one or moresegments of a content item along the overlay network, the overlaynetwork topology including a plurality of nodes of the overlay networkand a plurality of links, each link logically interconnecting a pair ofthe plurality of nodes to facilitate the distribution of the one or moresegments between the pair of nodes along that link, the methodcomprising: (a) periodically processing a plurality of metrics duringsuccessive time periods, each metric reflecting, for each time period,an attribute associated with the nodes or links of a current overlaynetwork topology employed during that time period, to generate processedmetrics that reflect node-relaying attributes and link-relayingattributes of the current overlay network topology; (b) predicting,based upon the processed metrics associated with nodes and links of aprospective overlay network topology, the node-relaying capacity andlink-relaying capacity of those nodes and links; (c) determining, basedat least in part upon the predicted node-relaying capacity andlink-relaying capacity of those nodes and links, whether the prospectiveoverlay network topology satisfies the performance criteria.
 18. Themethod of claim 17, wherein the steps of determining the node-relayingcapacity and determining the link-relaying capacity comprise excludingcapacity-limited observations.
 19. The method of claim 17, whereinprocessing the plurality of metrics comprises filtering to excludedemand-limited observations, and wherein the processed metrics excludedemand-limited observations.
 20. The method of claim 17, furthercomprising determining an optimal overlay network topology based on theset of one or more application-specific performance criteria.
 21. Amethod for reconfiguring overlay network topologies over which contentitems are distributed, wherein each overlay network topology includes aplurality of network nodes and a plurality of links interconnecting theplurality of network nodes; the method comprising the following steps:(a) generating a plurality of metrics with respect to a current overlaynetwork topology; (b) filtering the plurality of metrics to excludedemand-limited observations; (c) generating, based upon the filteredplurality of metrics, a plurality of predictions regarding a set ofspecified nodes and links; and (d) identifying a prospective overlaynetwork topology based upon the plurality of predictions.
 22. The methodof claim 21, wherein the prospective overlay network topology satisfiesperformance criteria representing one or more constraints on theperformance of the specified nodes and links.
 23. The method of claim21, wherein each overlay network topology is a peer-based overlaynetwork topology.
 24. The method of claim 21, further comprising thestep of generating viewer indicator predictions indicating whether eachnetwork node will be part of the prospective overlay network topology.25. The method of claim 24, further comprising the step of generatingsession duration predictions indicating the duration of time duringwhich those network nodes that will be part of the prospective overlaynetwork topology will remain part of the prospective overlay networktopology.
 26. The method of claim 21, wherein the plurality ofpredictions include predictions of the node-relaying capacity of thespecified nodes and the link-relaying capacity of the specified links.27. The method of claim 21, wherein the plurality of metrics includenode metrics, link metrics and a timestamp during which the node metricsand link metrics were obtained.
 28. The method of claim 21, wherein thesteps of determining the node-relaying capacity and determining thelink-relaying capacity comprise excluding capacity-limited observations.29. The method of claim 21, further comprising generating an optimaloverlay network topology that satisfies the performance criteria.
 30. Anadaptive topology server that reconfigures overlay network topologiesover which content items are distributed, wherein each overlay networktopology includes a plurality of network nodes and a plurality of linksinterconnecting the plurality of network nodes; the adaptive topologyserver comprising: (a) a metrics processor that generates a plurality ofmetrics with respect to a current overlay network topology, wherein theplurality of metrics exclude demand-limited observations; (b) aprediction engine that generates, based upon the plurality of metrics, aplurality of predictions regarding a set of specified nodes and links;and (c) a topology selector that obtains from the prediction engine aplurality of predictions with respect to the set of specified nodes andlinks and identifies a prospective overlay network topology based uponthe plurality of predictions.
 31. The adaptive topology server of claim30, wherein the topology selector identifies a prospective overlaynetwork topology that satisfies performance criteria representing one ormore performance constraints.
 32. The adaptive topology server of claim30, wherein each overlay network topology is a peer-based overlaynetwork topology.
 33. The adaptive topology server of claim 30, whereinthe prediction engine generates viewer indicator predictions indicatingwhether each network node will be part of the prospective overlaynetwork topology.
 34. The adaptive topology server of claim 30, whereinthe prediction engine generates session duration predictions indicatingthe duration of time during which those network nodes that will be partof the prospective overlay network topology will remain part of theprospective overlay network topology.
 35. The adaptive topology serverof claim 30, wherein the prediction engine generates the node-relayingcapacity of the specified nodes and the link-relaying capacity of thespecified links.
 36. The adaptive topology server of claim 30, whereinthe plurality of metrics generated by the metrics processor includesnode metrics, link metrics and a timestamp during which the node metricsand link metrics were obtained.
 37. The adaptive topology server ofclaim 30, wherein the topology determines an optimal overlay networktopology based on the set of one or more application-specificperformance criteria.
 38. The adaptive topology server of claim 30,wherein the steps of determining the node-relaying capacity anddetermining the link-relaying capacity comprise excludingcapacity-limited observations.